Overview

Brought to you by YData

Dataset statistics

Number of variables65
Number of observations457133
Missing cells13007847
Missing cells (%)43.8%
Total size in memory226.7 MiB
Average record size in memory520.0 B

Variable types

Text65

Dataset

DescriptionFish NMNH Extant Specimen Records (USNM) 0055081-241126133413365
URLhttps://doi.org/10.15468/hnhrg3

Alerts

institutionID has constant value "urn:lsid:biocol.org:col:34871" Constant
collectionID has constant value "urn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f" Constant
institutionCode has constant value "USNM" Constant
collectionCode has constant value "FISH" Constant
datasetName has constant value "NMNH Extant Biology" Constant
sex has constant value "male" Constant
phylum has constant value "Chordata" Constant
taxonRank has constant value "subspecies" Constant
recordNumber has 436211 (95.4%) missing values Missing
recordedBy has 288530 (63.1%) missing values Missing
sex has 457130 (> 99.9%) missing values Missing
preparations has 347674 (76.1%) missing values Missing
associatedMedia has 362721 (79.3%) missing values Missing
associatedSequences has 456682 (99.9%) missing values Missing
occurrenceRemarks has 291702 (63.8%) missing values Missing
fieldNumber has 275385 (60.2%) missing values Missing
eventDate has 57561 (12.6%) missing values Missing
startDayOfYear has 78566 (17.2%) missing values Missing
endDayOfYear has 78355 (17.1%) missing values Missing
year has 57561 (12.6%) missing values Missing
month has 77602 (17.0%) missing values Missing
day has 91390 (20.0%) missing values Missing
verbatimEventDate has 92889 (20.3%) missing values Missing
locationID has 353466 (77.3%) missing values Missing
higherGeography has 20566 (4.5%) missing values Missing
continent has 23711 (5.2%) missing values Missing
waterBody has 133831 (29.3%) missing values Missing
islandGroup has 392466 (85.9%) missing values Missing
island has 271745 (59.4%) missing values Missing
country has 36040 (7.9%) missing values Missing
stateProvince has 175053 (38.3%) missing values Missing
county has 359033 (78.5%) missing values Missing
locality has 45318 (9.9%) missing values Missing
verbatimElevation has 454919 (99.5%) missing values Missing
minimumDepthInMeters has 250216 (54.7%) missing values Missing
maximumDepthInMeters has 265019 (58.0%) missing values Missing
verbatimDepth has 448526 (98.1%) missing values Missing
decimalLatitude has 255392 (55.9%) missing values Missing
decimalLongitude has 255392 (55.9%) missing values Missing
geodeticDatum has 449927 (98.4%) missing values Missing
coordinateUncertaintyInMeters has 451954 (98.9%) missing values Missing
verbatimLatitude has 276490 (60.5%) missing values Missing
verbatimLongitude has 276551 (60.5%) missing values Missing
verbatimCoordinateSystem has 310239 (67.9%) missing values Missing
georeferenceProtocol has 439678 (96.2%) missing values Missing
georeferenceRemarks has 434034 (94.9%) missing values Missing
identificationQualifier has 455433 (99.6%) missing values Missing
typeStatus has 437569 (95.7%) missing values Missing
identifiedBy has 422838 (92.5%) missing values Missing
genus has 23392 (5.1%) missing values Missing
subgenus has 456844 (99.9%) missing values Missing
specificEpithet has 67592 (14.8%) missing values Missing
infraspecificEpithet has 447182 (97.8%) missing values Missing
taxonRank has 447182 (97.8%) missing values Missing
scientificNameAuthorship has 417908 (91.4%) missing values Missing
gbifID has unique values Unique
occurrenceID has unique values Unique

Reproduction

Analysis started2025-03-26 20:28:46.341275
Analysis finished2025-03-26 20:28:59.332233
Duration12.99 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct457133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:28:59.550448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4571330
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique457133 ?
Unique (%)100.0%

Sample

1st row1317202656
2nd row1317202715
3rd row1322535976
4th row1317203467
5th row2235732924
ValueCountFrequency (%)
1317202656 1
 
< 0.1%
1317216362 1
 
< 0.1%
3758717622 1
 
< 0.1%
1317206835 1
 
< 0.1%
1322539466 1
 
< 0.1%
2235733055 1
 
< 0.1%
1322541352 1
 
< 0.1%
1843575433 1
 
< 0.1%
1843575436 1
 
< 0.1%
1322545228 1
 
< 0.1%
Other values (457123) 457123
> 99.9%
2025-03-26T16:28:59.858883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 958747
21.0%
3 717804
15.7%
2 574384
12.6%
8 369171
 
8.1%
0 350125
 
7.7%
9 348079
 
7.6%
7 346850
 
7.6%
4 315488
 
6.9%
5 303498
 
6.6%
6 287184
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4571330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 958747
21.0%
3 717804
15.7%
2 574384
12.6%
8 369171
 
8.1%
0 350125
 
7.7%
9 348079
 
7.6%
7 346850
 
7.6%
4 315488
 
6.9%
5 303498
 
6.6%
6 287184
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4571330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 958747
21.0%
3 717804
15.7%
2 574384
12.6%
8 369171
 
8.1%
0 350125
 
7.7%
9 348079
 
7.6%
7 346850
 
7.6%
4 315488
 
6.9%
5 303498
 
6.6%
6 287184
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4571330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 958747
21.0%
3 717804
15.7%
2 574384
12.6%
8 369171
 
8.1%
0 350125
 
7.7%
9 348079
 
7.6%
7 346850
 
7.6%
4 315488
 
6.9%
5 303498
 
6.6%
6 287184
 
6.3%
Distinct55623
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:00.008565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters8685527
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31301 ?
Unique (%)6.8%

Sample

1st row2023-06-02 12:34:00
2nd row2019-11-27 11:21:00
3rd row2018-02-21 11:18:00
4th row2020-03-23 11:52:00
5th row2019-07-18 12:15:00
ValueCountFrequency (%)
2014-08-20 47687
 
5.2%
2014-08-25 38627
 
4.2%
2014-08-26 18664
 
2.0%
2019-07-18 14270
 
1.6%
2020-02-03 9235
 
1.0%
2017-12-18 6873
 
0.8%
2017-03-27 6208
 
0.7%
2017-05-23 6051
 
0.7%
2014-08-19 5531
 
0.6%
2018-07-27 4636
 
0.5%
Other values (3687) 756484
82.7%
2025-03-26T16:29:00.199934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2232599
25.7%
1 1219367
14.0%
2 1147277
13.2%
- 914266
10.5%
: 914266
10.5%
457133
 
5.3%
4 382208
 
4.4%
8 315419
 
3.6%
3 306749
 
3.5%
5 268670
 
3.1%
Other values (3) 527573
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8685527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2232599
25.7%
1 1219367
14.0%
2 1147277
13.2%
- 914266
10.5%
: 914266
10.5%
457133
 
5.3%
4 382208
 
4.4%
8 315419
 
3.6%
3 306749
 
3.5%
5 268670
 
3.1%
Other values (3) 527573
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8685527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2232599
25.7%
1 1219367
14.0%
2 1147277
13.2%
- 914266
10.5%
: 914266
10.5%
457133
 
5.3%
4 382208
 
4.4%
8 315419
 
3.6%
3 306749
 
3.5%
5 268670
 
3.1%
Other values (3) 527573
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8685527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2232599
25.7%
1 1219367
14.0%
2 1147277
13.2%
- 914266
10.5%
: 914266
10.5%
457133
 
5.3%
4 382208
 
4.4%
8 315419
 
3.6%
3 306749
 
3.5%
5 268670
 
3.1%
Other values (3) 527573
 
6.1%

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:00.245322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters13256857
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:lsid:biocol.org:col:34871
2nd rowurn:lsid:biocol.org:col:34871
3rd rowurn:lsid:biocol.org:col:34871
4th rowurn:lsid:biocol.org:col:34871
5th rowurn:lsid:biocol.org:col:34871
ValueCountFrequency (%)
urn:lsid:biocol.org:col:34871 457133
100.0%
2025-03-26T16:29:00.328680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1828532
13.8%
: 1828532
13.8%
l 1371399
 
10.3%
i 914266
 
6.9%
r 914266
 
6.9%
c 914266
 
6.9%
g 457133
 
3.4%
7 457133
 
3.4%
8 457133
 
3.4%
4 457133
 
3.4%
Other values (8) 3657064
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13256857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1828532
13.8%
: 1828532
13.8%
l 1371399
 
10.3%
i 914266
 
6.9%
r 914266
 
6.9%
c 914266
 
6.9%
g 457133
 
3.4%
7 457133
 
3.4%
8 457133
 
3.4%
4 457133
 
3.4%
Other values (8) 3657064
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13256857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1828532
13.8%
: 1828532
13.8%
l 1371399
 
10.3%
i 914266
 
6.9%
r 914266
 
6.9%
c 914266
 
6.9%
g 457133
 
3.4%
7 457133
 
3.4%
8 457133
 
3.4%
4 457133
 
3.4%
Other values (8) 3657064
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13256857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1828532
13.8%
: 1828532
13.8%
l 1371399
 
10.3%
i 914266
 
6.9%
r 914266
 
6.9%
c 914266
 
6.9%
g 457133
 
3.4%
7 457133
 
3.4%
8 457133
 
3.4%
4 457133
 
3.4%
Other values (8) 3657064
27.6%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:00.358676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters20570985
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
2nd rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
3rd rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
4th rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
5th rowurn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f
ValueCountFrequency (%)
urn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f 457133
100.0%
2025-03-26T16:29:00.433948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 2742798
13.3%
f 2285665
11.1%
9 1828532
8.9%
- 1828532
8.9%
d 1828532
8.9%
b 1371399
 
6.7%
5 1371399
 
6.7%
u 1371399
 
6.7%
: 914266
 
4.4%
3 914266
 
4.4%
Other values (8) 4114197
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20570985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2742798
13.3%
f 2285665
11.1%
9 1828532
8.9%
- 1828532
8.9%
d 1828532
8.9%
b 1371399
 
6.7%
5 1371399
 
6.7%
u 1371399
 
6.7%
: 914266
 
4.4%
3 914266
 
4.4%
Other values (8) 4114197
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20570985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2742798
13.3%
f 2285665
11.1%
9 1828532
8.9%
- 1828532
8.9%
d 1828532
8.9%
b 1371399
 
6.7%
5 1371399
 
6.7%
u 1371399
 
6.7%
: 914266
 
4.4%
3 914266
 
4.4%
Other values (8) 4114197
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20570985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2742798
13.3%
f 2285665
11.1%
9 1828532
8.9%
- 1828532
8.9%
d 1828532
8.9%
b 1371399
 
6.7%
5 1371399
 
6.7%
u 1371399
 
6.7%
: 914266
 
4.4%
3 914266
 
4.4%
Other values (8) 4114197
20.0%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:00.523219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1828532
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSNM
2nd rowUSNM
3rd rowUSNM
4th rowUSNM
5th rowUSNM
ValueCountFrequency (%)
usnm 457133
100.0%
2025-03-26T16:29:00.597423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 457133
25.0%
S 457133
25.0%
N 457133
25.0%
M 457133
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1828532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 457133
25.0%
S 457133
25.0%
N 457133
25.0%
M 457133
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1828532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 457133
25.0%
S 457133
25.0%
N 457133
25.0%
M 457133
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1828532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 457133
25.0%
S 457133
25.0%
N 457133
25.0%
M 457133
25.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:00.623642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1828532
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFISH
2nd rowFISH
3rd rowFISH
4th rowFISH
5th rowFISH
ValueCountFrequency (%)
fish 457133
100.0%
2025-03-26T16:29:00.698939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 457133
25.0%
I 457133
25.0%
S 457133
25.0%
H 457133
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1828532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 457133
25.0%
I 457133
25.0%
S 457133
25.0%
H 457133
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1828532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 457133
25.0%
I 457133
25.0%
S 457133
25.0%
H 457133
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1828532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 457133
25.0%
I 457133
25.0%
S 457133
25.0%
H 457133
25.0%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:00.727201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters8685527
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNMNH Extant Biology
2nd rowNMNH Extant Biology
3rd rowNMNH Extant Biology
4th rowNMNH Extant Biology
5th rowNMNH Extant Biology
ValueCountFrequency (%)
nmnh 457133
33.3%
extant 457133
33.3%
biology 457133
33.3%
2025-03-26T16:29:00.802906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 914266
 
10.5%
914266
 
10.5%
t 914266
 
10.5%
o 914266
 
10.5%
M 457133
 
5.3%
H 457133
 
5.3%
E 457133
 
5.3%
x 457133
 
5.3%
a 457133
 
5.3%
n 457133
 
5.3%
Other values (5) 2285665
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8685527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 914266
 
10.5%
914266
 
10.5%
t 914266
 
10.5%
o 914266
 
10.5%
M 457133
 
5.3%
H 457133
 
5.3%
E 457133
 
5.3%
x 457133
 
5.3%
a 457133
 
5.3%
n 457133
 
5.3%
Other values (5) 2285665
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8685527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 914266
 
10.5%
914266
 
10.5%
t 914266
 
10.5%
o 914266
 
10.5%
M 457133
 
5.3%
H 457133
 
5.3%
E 457133
 
5.3%
x 457133
 
5.3%
a 457133
 
5.3%
n 457133
 
5.3%
Other values (5) 2285665
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8685527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 914266
 
10.5%
914266
 
10.5%
t 914266
 
10.5%
o 914266
 
10.5%
M 457133
 
5.3%
H 457133
 
5.3%
E 457133
 
5.3%
x 457133
 
5.3%
a 457133
 
5.3%
n 457133
 
5.3%
Other values (5) 2285665
26.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:00.831861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length17
Mean length17.08124769
Min length17

Characters and Unicode

Total characters7808402
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPreservedSpecimen
2nd rowPreservedSpecimen
3rd rowPreservedSpecimen
4th rowPreservedSpecimen
5th rowMachineObservation
ValueCountFrequency (%)
preservedspecimen 419992
91.9%
machineobservation 37141
 
8.1%
2025-03-26T16:29:00.910365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2174242
27.8%
r 877125
11.2%
i 494274
 
6.3%
n 494274
 
6.3%
c 457133
 
5.9%
s 457133
 
5.9%
v 457133
 
5.9%
m 419992
 
5.4%
P 419992
 
5.4%
p 419992
 
5.4%
Other values (9) 1137112
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7808402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2174242
27.8%
r 877125
11.2%
i 494274
 
6.3%
n 494274
 
6.3%
c 457133
 
5.9%
s 457133
 
5.9%
v 457133
 
5.9%
m 419992
 
5.4%
P 419992
 
5.4%
p 419992
 
5.4%
Other values (9) 1137112
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7808402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2174242
27.8%
r 877125
11.2%
i 494274
 
6.3%
n 494274
 
6.3%
c 457133
 
5.9%
s 457133
 
5.9%
v 457133
 
5.9%
m 419992
 
5.4%
P 419992
 
5.4%
p 419992
 
5.4%
Other values (9) 1137112
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7808402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2174242
27.8%
r 877125
11.2%
i 494274
 
6.3%
n 494274
 
6.3%
c 457133
 
5.9%
s 457133
 
5.9%
v 457133
 
5.9%
m 419992
 
5.4%
P 419992
 
5.4%
p 419992
 
5.4%
Other values (9) 1137112
14.6%

occurrenceID
Text

Unique 

Distinct457133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-03-26T16:29:01.090033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters28799379
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique457133 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/30002bab5-5433-4b6c-8496-286a4a697fd7
2nd rowhttp://n2t.net/ark:/65665/3000315ff-b613-4f47-813c-5c48d8e0a883
3rd rowhttp://n2t.net/ark:/65665/3ebef4ab3-d946-4961-9221-c7c9692640f8
4th rowhttp://n2t.net/ark:/65665/3000bbb81-e139-47f8-b2bc-db762804769d
5th rowhttp://n2t.net/ark:/65665/3002333ca-4702-4d0d-93cd-265885eff56a
ValueCountFrequency (%)
http://n2t.net/ark:/65665/30002bab5-5433-4b6c-8496-286a4a697fd7 1
 
< 0.1%
http://n2t.net/ark:/65665/3009c8a06-9eea-4297-a94a-ca0a7f7c4523 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec0c9d7d-35a5-4841-8a5f-f74ef9113d06 1
 
< 0.1%
http://n2t.net/ark:/65665/300319b93-d6b8-4b79-b23d-d3825483b706 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec168a54-17b9-4a71-9ecc-92d446311c64 1
 
< 0.1%
http://n2t.net/ark:/65665/300370a00-b7af-441b-87cd-9c14a7b5b464 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec2b37a7-5b92-4aa2-ad75-a247e8e353f8 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec3b1e55-b813-49b8-83c7-eadc9323514e 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec3f4a3d-f79a-4cba-b8e4-022b57aa27d6 1
 
< 0.1%
http://n2t.net/ark:/65665/3ec57b74b-7fc8-4006-ad93-945fb0784573 1
 
< 0.1%
Other values (457123) 457123
> 99.9%
2025-03-26T16:29:01.332444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2285665
 
7.9%
6 2228380
 
7.7%
- 1828532
 
6.3%
t 1828532
 
6.3%
5 1770094
 
6.1%
a 1429386
 
5.0%
2 1314273
 
4.6%
4 1314182
 
4.6%
e 1314112
 
4.6%
3 1313982
 
4.6%
Other values (16) 12172241
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28799379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 2285665
 
7.9%
6 2228380
 
7.7%
- 1828532
 
6.3%
t 1828532
 
6.3%
5 1770094
 
6.1%
a 1429386
 
5.0%
2 1314273
 
4.6%
4 1314182
 
4.6%
e 1314112
 
4.6%
3 1313982
 
4.6%
Other values (16) 12172241
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28799379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 2285665
 
7.9%
6 2228380
 
7.7%
- 1828532
 
6.3%
t 1828532
 
6.3%
5 1770094
 
6.1%
a 1429386
 
5.0%
2 1314273
 
4.6%
4 1314182
 
4.6%
e 1314112
 
4.6%
3 1313982
 
4.6%
Other values (16) 12172241
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28799379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 2285665
 
7.9%
6 2228380
 
7.7%
- 1828532
 
6.3%
t 1828532
 
6.3%
5 1770094
 
6.1%
a 1429386
 
5.0%
2 1314273
 
4.6%
4 1314182
 
4.6%
e 1314112
 
4.6%
3 1313982
 
4.6%
Other values (16) 12172241
42.3%
Distinct457125
Distinct (%)> 99.9%
Missing3
Missing (%)< 0.1%
Memory size3.5 MiB
2025-03-26T16:29:01.675708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.0460788
Min length6

Characters and Unicode

Total characters5049494
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique457120 ?
Unique (%)> 99.9%

Sample

1st rowUSNM 51082
2nd rowUSNM 110432
3rd rowUSNM 49860
4th rowUSNM 239751
5th rowUSNM RAD122557
ValueCountFrequency (%)
usnm 457130
50.0%
114351 2
 
< 0.1%
rad125895 2
 
< 0.1%
465983 2
 
< 0.1%
135878 2
 
< 0.1%
466814 2
 
< 0.1%
fin30680 1
 
< 0.1%
253658 1
 
< 0.1%
314782 1
 
< 0.1%
97025 1
 
< 0.1%
Other values (457116) 457116
50.0%
2025-03-26T16:29:02.057302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 466881
 
9.2%
U 457130
 
9.1%
M 457130
 
9.1%
457130
 
9.1%
S 457130
 
9.1%
1 350154
 
6.9%
2 336599
 
6.7%
3 331818
 
6.6%
4 287753
 
5.7%
6 229742
 
4.5%
Other values (10) 1218027
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5049494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 466881
 
9.2%
U 457130
 
9.1%
M 457130
 
9.1%
457130
 
9.1%
S 457130
 
9.1%
1 350154
 
6.9%
2 336599
 
6.7%
3 331818
 
6.6%
4 287753
 
5.7%
6 229742
 
4.5%
Other values (10) 1218027
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5049494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 466881
 
9.2%
U 457130
 
9.1%
M 457130
 
9.1%
457130
 
9.1%
S 457130
 
9.1%
1 350154
 
6.9%
2 336599
 
6.7%
3 331818
 
6.6%
4 287753
 
5.7%
6 229742
 
4.5%
Other values (10) 1218027
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5049494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 466881
 
9.2%
U 457130
 
9.1%
M 457130
 
9.1%
457130
 
9.1%
S 457130
 
9.1%
1 350154
 
6.9%
2 336599
 
6.7%
3 331818
 
6.6%
4 287753
 
5.7%
6 229742
 
4.5%
Other values (10) 1218027
24.1%

recordNumber
Text

Missing 

Distinct20910
Distinct (%)99.9%
Missing436211
Missing (%)95.4%
Memory size3.5 MiB
2025-03-26T16:29:02.100302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length8
Mean length8.388060415
Min length1

Characters and Unicode

Total characters175495
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20899 ?
Unique (%)99.9%

Sample

1st rowPHISH-032
2nd rowAUST-251
3rd rowMOC11646
4th rowRP-202
5th rowSCIL-052
ValueCountFrequency (%)
blz 1437
 
5.5%
bah 714
 
2.8%
tci 687
 
2.7%
sms 537
 
2.1%
cur 428
 
1.7%
tob 394
 
1.5%
twn 281
 
1.1%
hbb 157
 
0.6%
fcc 148
 
0.6%
keb&mgg 111
 
0.4%
Other values (19067) 21018
81.1%
2025-03-26T16:29:02.190441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16746
 
9.5%
0 13243
 
7.5%
- 11115
 
6.3%
2 9698
 
5.5%
3 7532
 
4.3%
7 6760
 
3.9%
4 6629
 
3.8%
9 6333
 
3.6%
S 6099
 
3.5%
I 5719
 
3.3%
Other values (54) 85621
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 175495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 16746
 
9.5%
0 13243
 
7.5%
- 11115
 
6.3%
2 9698
 
5.5%
3 7532
 
4.3%
7 6760
 
3.9%
4 6629
 
3.8%
9 6333
 
3.6%
S 6099
 
3.5%
I 5719
 
3.3%
Other values (54) 85621
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 175495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 16746
 
9.5%
0 13243
 
7.5%
- 11115
 
6.3%
2 9698
 
5.5%
3 7532
 
4.3%
7 6760
 
3.9%
4 6629
 
3.8%
9 6333
 
3.6%
S 6099
 
3.5%
I 5719
 
3.3%
Other values (54) 85621
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 175495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 16746
 
9.5%
0 13243
 
7.5%
- 11115
 
6.3%
2 9698
 
5.5%
3 7532
 
4.3%
7 6760
 
3.9%
4 6629
 
3.8%
9 6333
 
3.6%
S 6099
 
3.5%
I 5719
 
3.3%
Other values (54) 85621
48.8%

recordedBy
Text

Missing 

Distinct7896
Distinct (%)4.7%
Missing288530
Missing (%)63.1%
Memory size3.5 MiB
2025-03-26T16:29:02.315785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length240
Median length115
Mean length26.11480223
Min length1

Characters and Unicode

Total characters4403034
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3027 ?
Unique (%)1.8%

Sample

1st rowJ. Snyder
2nd rowD. Richardson
3rd rowSmithsonian Team, A. Alcala & Silliman University Group
4th rowBronson
5th rowG. Hendler
ValueCountFrequency (%)
77976
 
9.1%
j 42927
 
5.0%
m 37012
 
4.3%
d 28416
 
3.3%
r 27723
 
3.2%
c 22240
 
2.6%
l 20228
 
2.3%
h 19709
 
2.3%
s 18441
 
2.1%
a 17847
 
2.1%
Other values (4989) 548640
63.7%
2025-03-26T16:29:02.512635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
692556
15.7%
. 356447
 
8.1%
e 288210
 
6.5%
a 269012
 
6.1%
r 202941
 
4.6%
n 202683
 
4.6%
i 199641
 
4.5%
o 171651
 
3.9%
l 162529
 
3.7%
t 157225
 
3.6%
Other values (66) 1700139
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4403034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
692556
15.7%
. 356447
 
8.1%
e 288210
 
6.5%
a 269012
 
6.1%
r 202941
 
4.6%
n 202683
 
4.6%
i 199641
 
4.5%
o 171651
 
3.9%
l 162529
 
3.7%
t 157225
 
3.6%
Other values (66) 1700139
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4403034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
692556
15.7%
. 356447
 
8.1%
e 288210
 
6.5%
a 269012
 
6.1%
r 202941
 
4.6%
n 202683
 
4.6%
i 199641
 
4.5%
o 171651
 
3.9%
l 162529
 
3.7%
t 157225
 
3.6%
Other values (66) 1700139
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4403034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
692556
15.7%
. 356447
 
8.1%
e 288210
 
6.5%
a 269012
 
6.1%
r 202941
 
4.6%
n 202683
 
4.6%
i 199641
 
4.5%
o 171651
 
3.9%
l 162529
 
3.7%
t 157225
 
3.6%
Other values (66) 1700139
38.6%
Distinct619
Distinct (%)0.1%
Missing15
Missing (%)< 0.1%
Memory size3.5 MiB
2025-03-26T16:29:02.551320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.12105627
Min length1

Characters and Unicode

Total characters512455
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique251 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row9
4th row12
5th row1
ValueCountFrequency (%)
1 246708
54.0%
2 61991
 
13.6%
3 30864
 
6.8%
4 19519
 
4.3%
5 14162
 
3.1%
6 10302
 
2.3%
7 7488
 
1.6%
10 6803
 
1.5%
8 6086
 
1.3%
9 4876
 
1.1%
Other values (609) 48319
 
10.6%
2025-03-26T16:29:02.633807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 280651
54.8%
2 78816
 
15.4%
3 40104
 
7.8%
4 26389
 
5.1%
5 23389
 
4.6%
0 18752
 
3.7%
6 14942
 
2.9%
7 11667
 
2.3%
8 9590
 
1.9%
9 8155
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 512455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 280651
54.8%
2 78816
 
15.4%
3 40104
 
7.8%
4 26389
 
5.1%
5 23389
 
4.6%
0 18752
 
3.7%
6 14942
 
2.9%
7 11667
 
2.3%
8 9590
 
1.9%
9 8155
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 512455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 280651
54.8%
2 78816
 
15.4%
3 40104
 
7.8%
4 26389
 
5.1%
5 23389
 
4.6%
0 18752
 
3.7%
6 14942
 
2.9%
7 11667
 
2.3%
8 9590
 
1.9%
9 8155
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 512455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 280651
54.8%
2 78816
 
15.4%
3 40104
 
7.8%
4 26389
 
5.1%
5 23389
 
4.6%
0 18752
 
3.7%
6 14942
 
2.9%
7 11667
 
2.3%
8 9590
 
1.9%
9 8155
 
1.6%

sex
Text

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing457130
Missing (%)> 99.9%
Memory size3.5 MiB
2025-03-26T16:29:02.659806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
ValueCountFrequency (%)
male 3
100.0%
2025-03-26T16:29:02.737316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 3
25.0%
a 3
25.0%
l 3
25.0%
e 3
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 3
25.0%
a 3
25.0%
l 3
25.0%
e 3
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 3
25.0%
a 3
25.0%
l 3
25.0%
e 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 3
25.0%
a 3
25.0%
l 3
25.0%
e 3
25.0%

preparations
Text

Missing 

Distinct325
Distinct (%)0.3%
Missing347674
Missing (%)76.1%
Memory size3.5 MiB
2025-03-26T16:29:02.779858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length192
Mean length11.80346066
Min length4

Characters and Unicode

Total characters1291995
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)0.1%

Sample

1st rowDry Osteological Specimen
2nd rowGlycerin with Bone Stain
3rd rowPolyester
4th rowLarvae [ETOH Fixed]
5th rowUnknown
ValueCountFrequency (%)
larvae 25741
14.6%
polyester 20129
 
11.4%
photograph 14126
 
8.0%
unknown 11559
 
6.6%
film 9653
 
5.5%
specimen 8100
 
4.6%
osteological 7064
 
4.0%
dry 7047
 
4.0%
glycerin 7045
 
4.0%
with 7043
 
4.0%
Other values (60) 58485
33.2%
2025-03-26T16:29:02.897526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 131056
 
10.1%
a 117704
 
9.1%
o 95334
 
7.4%
r 91873
 
7.1%
t 83498
 
6.5%
n 69921
 
5.4%
l 67018
 
5.2%
i 66898
 
5.2%
66533
 
5.1%
h 42041
 
3.3%
Other values (46) 460119
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1291995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 131056
 
10.1%
a 117704
 
9.1%
o 95334
 
7.4%
r 91873
 
7.1%
t 83498
 
6.5%
n 69921
 
5.4%
l 67018
 
5.2%
i 66898
 
5.2%
66533
 
5.1%
h 42041
 
3.3%
Other values (46) 460119
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1291995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 131056
 
10.1%
a 117704
 
9.1%
o 95334
 
7.4%
r 91873
 
7.1%
t 83498
 
6.5%
n 69921
 
5.4%
l 67018
 
5.2%
i 66898
 
5.2%
66533
 
5.1%
h 42041
 
3.3%
Other values (46) 460119
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1291995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 131056
 
10.1%
a 117704
 
9.1%
o 95334
 
7.4%
r 91873
 
7.1%
t 83498
 
6.5%
n 69921
 
5.4%
l 67018
 
5.2%
i 66898
 
5.2%
66533
 
5.1%
h 42041
 
3.3%
Other values (46) 460119
35.6%

associatedMedia
Text

Missing 

Distinct59911
Distinct (%)63.5%
Missing362721
Missing (%)79.3%
Memory size3.5 MiB
2025-03-26T16:29:02.982855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length579
Median length49
Mean length55.87274923
Min length48

Characters and Unicode

Total characters5275058
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52560 ?
Unique (%)55.7%

Sample

1st rowhttps://collections.nmnh.si.edu/media/?i=10977965
2nd rowhttps://collections.nmnh.si.edu/media/?i=13093375
3rd rowhttps://collections.nmnh.si.edu/media/?i=5000406; 14894714
4th rowhttps://collections.nmnh.si.edu/media/?i=13314074
5th rowhttps://collections.nmnh.si.edu/media/?i=12868276
ValueCountFrequency (%)
14558510 3144
 
1.9%
14894714 3131
 
1.9%
14888503 2892
 
1.8%
14888504 2091
 
1.3%
5000376 2043
 
1.3%
5000375 2043
 
1.3%
15777181 1447
 
0.9%
15596573 1421
 
0.9%
5000606 1256
 
0.8%
https://collections.nmnh.si.edu/media/?i=5004168 1242
 
0.8%
Other values (84942) 142576
87.3%
2025-03-26T16:29:03.152213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 377648
 
7.2%
i 377648
 
7.2%
e 283236
 
5.4%
n 283236
 
5.4%
s 283236
 
5.4%
t 283236
 
5.4%
. 283236
 
5.4%
1 239447
 
4.5%
0 222278
 
4.2%
d 188824
 
3.6%
Other values (21) 2453033
46.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5275058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 377648
 
7.2%
i 377648
 
7.2%
e 283236
 
5.4%
n 283236
 
5.4%
s 283236
 
5.4%
t 283236
 
5.4%
. 283236
 
5.4%
1 239447
 
4.5%
0 222278
 
4.2%
d 188824
 
3.6%
Other values (21) 2453033
46.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5275058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 377648
 
7.2%
i 377648
 
7.2%
e 283236
 
5.4%
n 283236
 
5.4%
s 283236
 
5.4%
t 283236
 
5.4%
. 283236
 
5.4%
1 239447
 
4.5%
0 222278
 
4.2%
d 188824
 
3.6%
Other values (21) 2453033
46.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5275058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 377648
 
7.2%
i 377648
 
7.2%
e 283236
 
5.4%
n 283236
 
5.4%
s 283236
 
5.4%
t 283236
 
5.4%
. 283236
 
5.4%
1 239447
 
4.5%
0 222278
 
4.2%
d 188824
 
3.6%
Other values (21) 2453033
46.5%

associatedSequences
Text

Missing 

Distinct448
Distinct (%)99.3%
Missing456682
Missing (%)99.9%
Memory size3.5 MiB
2025-03-26T16:29:03.202214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length249
Median length49
Mean length59.86474501
Min length49

Characters and Unicode

Total characters26999
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique445 ?
Unique (%)98.7%

Sample

1st rowhttps://www.ncbi.nlm.nih.gov/gquery?term=FJ609901
2nd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=HQ600890
3rd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=HM748411
4th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=HQ600884
5th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MN621852
ValueCountFrequency (%)
https://www.ncbi.nlm.nih.gov/gquery?term=mn549761 2
 
0.4%
https://www.ncbi.nlm.nih.gov/gquery?term=hq543050 2
 
0.4%
https://www.ncbi.nlm.nih.gov/gquery?term=hq543049 2
 
0.4%
https://www.ncbi.nlm.nih.gov/gquery?term=fj609858 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=hq543043 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=hq600884 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=mn621852 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=hq325698|https://www.ncbi.nlm.nih.gov/gquery?term=hq325631 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=hm748370 1
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=ef536294|https://www.ncbi.nlm.nih.gov/gquery?term=ef536256|https://www.ncbi.nlm.nih.gov/gquery?term=ef539241|https://www.ncbi.nlm.nih.gov/gquery?term=ef533917|https://www.ncbi.nlm.nih.gov/gquery?term=ef530094 1
 
0.2%
Other values (438) 438
97.1%
2025-03-26T16:29:03.303066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2196
 
8.1%
t 1647
 
6.1%
/ 1647
 
6.1%
w 1647
 
6.1%
n 1647
 
6.1%
h 1098
 
4.1%
r 1098
 
4.1%
e 1098
 
4.1%
i 1098
 
4.1%
m 1098
 
4.1%
Other values (41) 12725
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2196
 
8.1%
t 1647
 
6.1%
/ 1647
 
6.1%
w 1647
 
6.1%
n 1647
 
6.1%
h 1098
 
4.1%
r 1098
 
4.1%
e 1098
 
4.1%
i 1098
 
4.1%
m 1098
 
4.1%
Other values (41) 12725
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2196
 
8.1%
t 1647
 
6.1%
/ 1647
 
6.1%
w 1647
 
6.1%
n 1647
 
6.1%
h 1098
 
4.1%
r 1098
 
4.1%
e 1098
 
4.1%
i 1098
 
4.1%
m 1098
 
4.1%
Other values (41) 12725
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2196
 
8.1%
t 1647
 
6.1%
/ 1647
 
6.1%
w 1647
 
6.1%
n 1647
 
6.1%
h 1098
 
4.1%
r 1098
 
4.1%
e 1098
 
4.1%
i 1098
 
4.1%
m 1098
 
4.1%
Other values (41) 12725
47.1%

occurrenceRemarks
Text

Missing 

Distinct80614
Distinct (%)48.7%
Missing291702
Missing (%)63.8%
Memory size3.5 MiB
2025-03-26T16:29:03.449680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1358
Median length916
Mean length60.06201377
Min length1

Characters and Unicode

Total characters9936119
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70906 ?
Unique (%)42.9%

Sample

1st rowNote in ledger: " pair of otoliths"; Otoliths are stored in the Osteo Collection.; Stored in Osteo Collection.; The ototliths are stored in Mugil Box 1 of 1, which contains catalog numbers: 110428, 110429, 110430, 110431, 110432, 110433, 110434, 110435, 110436, 110438, 110439, 110440, and 110441.
2nd rowCat. no. 105
3rd rowHost-bohadschia argus. rec from: truett, d. f.
4th rowSpecimen measurements as written on the slide mount: SL (mm)= 205; TL (mm)= 10" (254); This material is part of the John and Helen Randall Slide Collection. The slides were digitized October 2017. The Randall donation includes all intellectual property rights.; Black paint/goop on the film. Not obscuring specimen.
5th rowSpecimen measurements as written on the slide mount: SL (mm)= 57; TL (mm)= 2.8" (71); This material is part of the John and Helen Randall Slide Collection. The slides were digitized October 2017. The Randall donation includes all intellectual property rights.
ValueCountFrequency (%)
the 73507
 
4.4%
of 47956
 
2.9%
in 34876
 
2.1%
and 29259
 
1.7%
mm 26667
 
1.6%
collection 24233
 
1.4%
specimen 23112
 
1.4%
as 22908
 
1.4%
is 22739
 
1.4%
1 19655
 
1.2%
Other values (62171) 1356660
80.7%
2025-03-26T16:29:03.675963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1516141
15.3%
e 848314
 
8.5%
t 538371
 
5.4%
i 537618
 
5.4%
a 526494
 
5.3%
n 525282
 
5.3%
o 516906
 
5.2%
l 413315
 
4.2%
s 411915
 
4.1%
r 389104
 
3.9%
Other values (97) 3712659
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9936119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1516141
15.3%
e 848314
 
8.5%
t 538371
 
5.4%
i 537618
 
5.4%
a 526494
 
5.3%
n 525282
 
5.3%
o 516906
 
5.2%
l 413315
 
4.2%
s 411915
 
4.1%
r 389104
 
3.9%
Other values (97) 3712659
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9936119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1516141
15.3%
e 848314
 
8.5%
t 538371
 
5.4%
i 537618
 
5.4%
a 526494
 
5.3%
n 525282
 
5.3%
o 516906
 
5.2%
l 413315
 
4.2%
s 411915
 
4.1%
r 389104
 
3.9%
Other values (97) 3712659
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9936119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1516141
15.3%
e 848314
 
8.5%
t 538371
 
5.4%
i 537618
 
5.4%
a 526494
 
5.3%
n 525282
 
5.3%
o 516906
 
5.2%
l 413315
 
4.2%
s 411915
 
4.1%
r 389104
 
3.9%
Other values (97) 3712659
37.4%

fieldNumber
Text

Missing 

Distinct25334
Distinct (%)13.9%
Missing275385
Missing (%)60.2%
Memory size3.5 MiB
2025-03-26T16:29:03.817178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length149
Median length70
Mean length10.07350837
Min length1

Characters and Unicode

Total characters1830840
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10528 ?
Unique (%)5.8%

Sample

1st rowFJS-455
2nd rowM10-97B4 (40-60m)
3rd rowSP 78-18
4th rowBBC 1734 A; M-84
5th rowPHISH-2016-05; SIA-06
ValueCountFrequency (%)
vgs 19366
 
5.7%
jtw 14350
 
4.2%
bbc 6147
 
1.8%
lwk 4289
 
1.3%
lk 4273
 
1.3%
sol 3430
 
1.0%
rpv 3304
 
1.0%
sp 3147
 
0.9%
bayley 2747
 
0.8%
lrp 2658
 
0.8%
Other values (22467) 276199
81.3%
2025-03-26T16:29:04.097530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 190790
 
10.4%
158162
 
8.6%
1 126060
 
6.9%
0 110499
 
6.0%
2 103800
 
5.7%
9 89715
 
4.9%
6 82440
 
4.5%
7 77168
 
4.2%
3 73429
 
4.0%
8 68931
 
3.8%
Other values (72) 749846
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1830840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 190790
 
10.4%
158162
 
8.6%
1 126060
 
6.9%
0 110499
 
6.0%
2 103800
 
5.7%
9 89715
 
4.9%
6 82440
 
4.5%
7 77168
 
4.2%
3 73429
 
4.0%
8 68931
 
3.8%
Other values (72) 749846
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1830840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 190790
 
10.4%
158162
 
8.6%
1 126060
 
6.9%
0 110499
 
6.0%
2 103800
 
5.7%
9 89715
 
4.9%
6 82440
 
4.5%
7 77168
 
4.2%
3 73429
 
4.0%
8 68931
 
3.8%
Other values (72) 749846
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1830840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 190790
 
10.4%
158162
 
8.6%
1 126060
 
6.9%
0 110499
 
6.0%
2 103800
 
5.7%
9 89715
 
4.9%
6 82440
 
4.5%
7 77168
 
4.2%
3 73429
 
4.0%
8 68931
 
3.8%
Other values (72) 749846
41.0%

eventDate
Text

Missing 

Distinct31169
Distinct (%)7.8%
Missing57561
Missing (%)12.6%
Memory size3.5 MiB
2025-03-26T16:29:04.237401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length10
Mean length10.1723319
Min length4

Characters and Unicode

Total characters4064579
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8396 ?
Unique (%)2.1%

Sample

1st row1938-03-25
2nd row1956-05-30
3rd row1997-05-10
4th row1978-05-22
5th row1928-02-10
ValueCountFrequency (%)
1906 1426
 
0.4%
1888 1093
 
0.3%
1902 1073
 
0.3%
1889 937
 
0.2%
1994-05-06 927
 
0.2%
1994-04-30 702
 
0.2%
1901 578
 
0.1%
1880 568
 
0.1%
1970-09-11/1970-09-16 512
 
0.1%
1893 443
 
0.1%
Other values (31130) 391513
97.9%
2025-03-26T16:29:04.449409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 783487
19.3%
1 744822
18.3%
0 657332
16.2%
9 538113
13.2%
2 291625
 
7.2%
8 219731
 
5.4%
6 186092
 
4.6%
7 185072
 
4.6%
5 153551
 
3.8%
3 143683
 
3.5%
Other values (6) 161071
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4064579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 783487
19.3%
1 744822
18.3%
0 657332
16.2%
9 538113
13.2%
2 291625
 
7.2%
8 219731
 
5.4%
6 186092
 
4.6%
7 185072
 
4.6%
5 153551
 
3.8%
3 143683
 
3.5%
Other values (6) 161071
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4064579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 783487
19.3%
1 744822
18.3%
0 657332
16.2%
9 538113
13.2%
2 291625
 
7.2%
8 219731
 
5.4%
6 186092
 
4.6%
7 185072
 
4.6%
5 153551
 
3.8%
3 143683
 
3.5%
Other values (6) 161071
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4064579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 783487
19.3%
1 744822
18.3%
0 657332
16.2%
9 538113
13.2%
2 291625
 
7.2%
8 219731
 
5.4%
6 186092
 
4.6%
7 185072
 
4.6%
5 153551
 
3.8%
3 143683
 
3.5%
Other values (6) 161071
 
4.0%

startDayOfYear
Text

Missing 

Distinct366
Distinct (%)0.1%
Missing78566
Missing (%)17.2%
Memory size3.5 MiB
2025-03-26T16:29:04.592065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.769520323
Min length1

Characters and Unicode

Total characters1048449
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row84
2nd row151
3rd row130
4th row142
5th row41
ValueCountFrequency (%)
120 2848
 
0.8%
126 2421
 
0.6%
243 2286
 
0.6%
152 2008
 
0.5%
151 2006
 
0.5%
251 2000
 
0.5%
90 1996
 
0.5%
212 1881
 
0.5%
117 1874
 
0.5%
146 1865
 
0.5%
Other values (356) 357382
94.4%
2025-03-26T16:29:04.786127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 212454
20.3%
2 190684
18.2%
3 135571
12.9%
5 84048
 
8.0%
4 83064
 
7.9%
6 77575
 
7.4%
0 70940
 
6.8%
7 67310
 
6.4%
9 65445
 
6.2%
8 61358
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1048449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 212454
20.3%
2 190684
18.2%
3 135571
12.9%
5 84048
 
8.0%
4 83064
 
7.9%
6 77575
 
7.4%
0 70940
 
6.8%
7 67310
 
6.4%
9 65445
 
6.2%
8 61358
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1048449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 212454
20.3%
2 190684
18.2%
3 135571
12.9%
5 84048
 
8.0%
4 83064
 
7.9%
6 77575
 
7.4%
0 70940
 
6.8%
7 67310
 
6.4%
9 65445
 
6.2%
8 61358
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1048449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 212454
20.3%
2 190684
18.2%
3 135571
12.9%
5 84048
 
8.0%
4 83064
 
7.9%
6 77575
 
7.4%
0 70940
 
6.8%
7 67310
 
6.4%
9 65445
 
6.2%
8 61358
 
5.9%

endDayOfYear
Text

Missing 

Distinct366
Distinct (%)0.1%
Missing78355
Missing (%)17.1%
Memory size3.5 MiB
2025-03-26T16:29:04.925590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.770578017
Min length1

Characters and Unicode

Total characters1049434
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row84
2nd row151
3rd row130
4th row142
5th row41
ValueCountFrequency (%)
120 2626
 
0.7%
243 2541
 
0.7%
126 2509
 
0.7%
151 2087
 
0.6%
251 1992
 
0.5%
90 1933
 
0.5%
117 1879
 
0.5%
212 1825
 
0.5%
161 1816
 
0.5%
181 1810
 
0.5%
Other values (356) 357760
94.5%
2025-03-26T16:29:05.132846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 212271
20.2%
2 190867
18.2%
3 136568
13.0%
5 84446
 
8.0%
4 82468
 
7.9%
6 77729
 
7.4%
0 70017
 
6.7%
7 67205
 
6.4%
9 66117
 
6.3%
8 61746
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1049434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 212271
20.2%
2 190867
18.2%
3 136568
13.0%
5 84446
 
8.0%
4 82468
 
7.9%
6 77729
 
7.4%
0 70017
 
6.7%
7 67205
 
6.4%
9 66117
 
6.3%
8 61746
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1049434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 212271
20.2%
2 190867
18.2%
3 136568
13.0%
5 84446
 
8.0%
4 82468
 
7.9%
6 77729
 
7.4%
0 70017
 
6.7%
7 67205
 
6.4%
9 66117
 
6.3%
8 61746
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1049434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 212271
20.2%
2 190867
18.2%
3 136568
13.0%
5 84446
 
8.0%
4 82468
 
7.9%
6 77729
 
7.4%
0 70017
 
6.7%
7 67205
 
6.4%
9 66117
 
6.3%
8 61746
 
5.9%

year
Text

Missing 

Distinct191
Distinct (%)< 0.1%
Missing57561
Missing (%)12.6%
Memory size3.5 MiB
2025-03-26T16:29:05.258935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1598288
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row1938
2nd row1956
3rd row1997
4th row1978
5th row1928
ValueCountFrequency (%)
1909 17795
 
4.5%
1908 13700
 
3.4%
1970 11256
 
2.8%
1969 10506
 
2.6%
1964 9126
 
2.3%
1978 9012
 
2.3%
1967 8414
 
2.1%
1979 7872
 
2.0%
1971 7870
 
2.0%
1968 7317
 
1.8%
Other values (181) 296704
74.3%
2025-03-26T16:29:05.433360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 436030
27.3%
1 416125
26.0%
0 151360
 
9.5%
8 134714
 
8.4%
7 104741
 
6.6%
6 101554
 
6.4%
2 84394
 
5.3%
5 60356
 
3.8%
4 57845
 
3.6%
3 51169
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1598288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 436030
27.3%
1 416125
26.0%
0 151360
 
9.5%
8 134714
 
8.4%
7 104741
 
6.6%
6 101554
 
6.4%
2 84394
 
5.3%
5 60356
 
3.8%
4 57845
 
3.6%
3 51169
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1598288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 436030
27.3%
1 416125
26.0%
0 151360
 
9.5%
8 134714
 
8.4%
7 104741
 
6.6%
6 101554
 
6.4%
2 84394
 
5.3%
5 60356
 
3.8%
4 57845
 
3.6%
3 51169
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1598288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 436030
27.3%
1 416125
26.0%
0 151360
 
9.5%
8 134714
 
8.4%
7 104741
 
6.6%
6 101554
 
6.4%
2 84394
 
5.3%
5 60356
 
3.8%
4 57845
 
3.6%
3 51169
 
3.2%

month
Text

Missing 

Distinct12
Distinct (%)< 0.1%
Missing77602
Missing (%)17.0%
Memory size3.5 MiB
2025-03-26T16:29:05.475366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.196294901
Min length1

Characters and Unicode

Total characters454031
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row5
5th row2
ValueCountFrequency (%)
5 47847
12.6%
6 38462
10.1%
9 38414
10.1%
8 35973
9.5%
4 35321
9.3%
7 34044
9.0%
3 33217
8.8%
11 31911
8.4%
10 24545
6.5%
2 22960
6.0%
Other values (2) 36837
9.7%
2025-03-26T16:29:05.560297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 125204
27.6%
5 47847
 
10.5%
2 41004
 
9.0%
6 38462
 
8.5%
9 38414
 
8.5%
8 35973
 
7.9%
4 35321
 
7.8%
7 34044
 
7.5%
3 33217
 
7.3%
0 24545
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 454031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 125204
27.6%
5 47847
 
10.5%
2 41004
 
9.0%
6 38462
 
8.5%
9 38414
 
8.5%
8 35973
 
7.9%
4 35321
 
7.8%
7 34044
 
7.5%
3 33217
 
7.3%
0 24545
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 454031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 125204
27.6%
5 47847
 
10.5%
2 41004
 
9.0%
6 38462
 
8.5%
9 38414
 
8.5%
8 35973
 
7.9%
4 35321
 
7.8%
7 34044
 
7.5%
3 33217
 
7.3%
0 24545
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 454031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 125204
27.6%
5 47847
 
10.5%
2 41004
 
9.0%
6 38462
 
8.5%
9 38414
 
8.5%
8 35973
 
7.9%
4 35321
 
7.8%
7 34044
 
7.5%
3 33217
 
7.3%
0 24545
 
5.4%

day
Text

Missing 

Distinct31
Distinct (%)< 0.1%
Missing91390
Missing (%)20.0%
Memory size3.5 MiB
2025-03-26T16:29:05.601299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.692724126
Min length1

Characters and Unicode

Total characters619102
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25
2nd row30
3rd row10
4th row22
5th row10
ValueCountFrequency (%)
8 14068
 
3.8%
15 13213
 
3.6%
6 12880
 
3.5%
7 12839
 
3.5%
23 12736
 
3.5%
5 12717
 
3.5%
11 12600
 
3.4%
3 12529
 
3.4%
16 12430
 
3.4%
14 12237
 
3.3%
Other values (21) 237494
64.9%
2025-03-26T16:29:05.695599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 162905
26.3%
2 152110
24.6%
3 52575
 
8.5%
5 37518
 
6.1%
8 37089
 
6.0%
6 36980
 
6.0%
4 36300
 
5.9%
7 36084
 
5.8%
9 33925
 
5.5%
0 33616
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 619102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 162905
26.3%
2 152110
24.6%
3 52575
 
8.5%
5 37518
 
6.1%
8 37089
 
6.0%
6 36980
 
6.0%
4 36300
 
5.9%
7 36084
 
5.8%
9 33925
 
5.5%
0 33616
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 619102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 162905
26.3%
2 152110
24.6%
3 52575
 
8.5%
5 37518
 
6.1%
8 37089
 
6.0%
6 36980
 
6.0%
4 36300
 
5.9%
7 36084
 
5.8%
9 33925
 
5.5%
0 33616
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 619102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 162905
26.3%
2 152110
24.6%
3 52575
 
8.5%
5 37518
 
6.1%
8 37089
 
6.0%
6 36980
 
6.0%
4 36300
 
5.9%
7 36084
 
5.8%
9 33925
 
5.5%
0 33616
 
5.4%

verbatimEventDate
Text

Missing 

Distinct34091
Distinct (%)9.4%
Missing92889
Missing (%)20.3%
Memory size3.5 MiB
2025-03-26T16:29:05.822642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length102
Median length98
Mean length26.64586376
Min length2

Characters and Unicode

Total characters9705596
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10967 ?
Unique (%)3.0%

Sample

1st row0000 00 00 - 0000 00 00
2nd row1938 Mar 25 - 0000 00 00
3rd row0000 00 00 - 0000 00 00
4th row1956 May 30 - 0000 00 00
5th row0000 00 00 - 0000 00 00
ValueCountFrequency (%)
00 800102
29.5%
422240
15.6%
0000 375334
13.9%
may 36246
 
1.3%
jun 32163
 
1.2%
sep 31820
 
1.2%
aug 30495
 
1.1%
apr 29211
 
1.1%
jul 27120
 
1.0%
mar 26277
 
1.0%
Other values (2487) 897275
33.1%
2025-03-26T16:29:06.018937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3548432
36.6%
2344039
24.2%
1 646882
 
6.7%
9 434088
 
4.5%
- 427789
 
4.4%
2 224216
 
2.3%
: 175852
 
1.8%
8 154478
 
1.6%
3 151943
 
1.6%
5 145152
 
1.5%
Other values (61) 1452725
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9705596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3548432
36.6%
2344039
24.2%
1 646882
 
6.7%
9 434088
 
4.5%
- 427789
 
4.4%
2 224216
 
2.3%
: 175852
 
1.8%
8 154478
 
1.6%
3 151943
 
1.6%
5 145152
 
1.5%
Other values (61) 1452725
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9705596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3548432
36.6%
2344039
24.2%
1 646882
 
6.7%
9 434088
 
4.5%
- 427789
 
4.4%
2 224216
 
2.3%
: 175852
 
1.8%
8 154478
 
1.6%
3 151943
 
1.6%
5 145152
 
1.5%
Other values (61) 1452725
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9705596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3548432
36.6%
2344039
24.2%
1 646882
 
6.7%
9 434088
 
4.5%
- 427789
 
4.4%
2 224216
 
2.3%
: 175852
 
1.8%
8 154478
 
1.6%
3 151943
 
1.6%
5 145152
 
1.5%
Other values (61) 1452725
15.0%

locationID
Text

Missing 

Distinct16432
Distinct (%)15.9%
Missing353466
Missing (%)77.3%
Memory size3.5 MiB
2025-03-26T16:29:06.145719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length68
Median length40
Mean length5.144829116
Min length1

Characters and Unicode

Total characters533349
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6094 ?
Unique (%)5.9%

Sample

1st rowM10-97B4 (4
2nd row4-31N
3rd row5627
4th row308
5th rowB12 TR4
ValueCountFrequency (%)
d 13108
 
9.8%
tc 3559
 
2.7%
haul 1248
 
0.9%
trans 1039
 
0.8%
1 920
 
0.7%
2 897
 
0.7%
tt 803
 
0.6%
4 665
 
0.5%
3 656
 
0.5%
5 629
 
0.5%
Other values (13813) 109746
82.3%
2025-03-26T16:29:06.342028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 62140
 
11.7%
2 49539
 
9.3%
- 37855
 
7.1%
3 36538
 
6.9%
4 36397
 
6.8%
5 34299
 
6.4%
0 31812
 
6.0%
29603
 
5.6%
7 29378
 
5.5%
6 27613
 
5.2%
Other values (65) 158175
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 533349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 62140
 
11.7%
2 49539
 
9.3%
- 37855
 
7.1%
3 36538
 
6.9%
4 36397
 
6.8%
5 34299
 
6.4%
0 31812
 
6.0%
29603
 
5.6%
7 29378
 
5.5%
6 27613
 
5.2%
Other values (65) 158175
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 533349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 62140
 
11.7%
2 49539
 
9.3%
- 37855
 
7.1%
3 36538
 
6.9%
4 36397
 
6.8%
5 34299
 
6.4%
0 31812
 
6.0%
29603
 
5.6%
7 29378
 
5.5%
6 27613
 
5.2%
Other values (65) 158175
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 533349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 62140
 
11.7%
2 49539
 
9.3%
- 37855
 
7.1%
3 36538
 
6.9%
4 36397
 
6.8%
5 34299
 
6.4%
0 31812
 
6.0%
29603
 
5.6%
7 29378
 
5.5%
6 27613
 
5.2%
Other values (65) 158175
29.7%

higherGeography
Text

Missing 

Distinct13774
Distinct (%)3.2%
Missing20566
Missing (%)4.5%
Memory size3.5 MiB
2025-03-26T16:29:06.470587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length177
Median length131
Mean length59.33453972
Min length4

Characters and Unicode

Total characters25903502
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3847 ?
Unique (%)0.9%

Sample

1st rowNorth Pacific Ocean, United States, Hawaii, Hawaiian Islands
2nd rowNorth Atlantic Ocean, Gulf of Mexico, United States, Florida, Hillsborough County
3rd rowNorth Pacific Ocean, Japan, Tokyo Prefecture, Japanese Archipelago, Honshu
4th rowNorth America, United States, West Virginia, Randolph County
5th rowAtlantic, Caribbean Sea, Barbados, Lesser Antilles, Barbados
ValueCountFrequency (%)
ocean 298904
 
8.7%
north 282797
 
8.2%
pacific 178831
 
5.2%
united 126094
 
3.7%
states 125850
 
3.7%
islands 125119
 
3.6%
atlantic 114299
 
3.3%
south 107409
 
3.1%
america 97219
 
2.8%
county 73130
 
2.1%
Other values (6601) 1904440
55.5%
2025-03-26T16:29:06.697592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2997525
 
11.6%
a 2689012
 
10.4%
i 1850186
 
7.1%
n 1676008
 
6.5%
e 1630116
 
6.3%
t 1404863
 
5.4%
, 1348548
 
5.2%
o 1193433
 
4.6%
c 1133403
 
4.4%
r 1090360
 
4.2%
Other values (113) 8890048
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25903502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2997525
 
11.6%
a 2689012
 
10.4%
i 1850186
 
7.1%
n 1676008
 
6.5%
e 1630116
 
6.3%
t 1404863
 
5.4%
, 1348548
 
5.2%
o 1193433
 
4.6%
c 1133403
 
4.4%
r 1090360
 
4.2%
Other values (113) 8890048
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25903502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2997525
 
11.6%
a 2689012
 
10.4%
i 1850186
 
7.1%
n 1676008
 
6.5%
e 1630116
 
6.3%
t 1404863
 
5.4%
, 1348548
 
5.2%
o 1193433
 
4.6%
c 1133403
 
4.4%
r 1090360
 
4.2%
Other values (113) 8890048
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25903502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2997525
 
11.6%
a 2689012
 
10.4%
i 1850186
 
7.1%
n 1676008
 
6.5%
e 1630116
 
6.3%
t 1404863
 
5.4%
, 1348548
 
5.2%
o 1193433
 
4.6%
c 1133403
 
4.4%
r 1090360
 
4.2%
Other values (113) 8890048
34.3%

continent
Text

Missing 

Distinct70
Distinct (%)< 0.1%
Missing23711
Missing (%)5.2%
Memory size3.5 MiB
2025-03-26T16:29:06.739934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length43
Mean length16.40105717
Min length4

Characters and Unicode

Total characters7108579
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowNorth Pacific Ocean
2nd rowNorth Atlantic Ocean
3rd rowNorth Pacific Ocean
4th rowNorth America
5th rowAtlantic
ValueCountFrequency (%)
ocean 297586
27.0%
north 272021
24.7%
pacific 178818
16.2%
atlantic 114202
 
10.4%
america 97219
 
8.8%
south 91155
 
8.3%
indian 28906
 
2.6%
asia 11417
 
1.0%
africa 6386
 
0.6%
europe 1543
 
0.1%
Other values (9) 2306
 
0.2%
2025-03-26T16:29:06.830840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 874298
12.3%
a 736555
10.4%
668137
9.4%
i 617296
 
8.7%
t 593407
 
8.3%
n 470618
 
6.6%
e 397361
 
5.6%
r 379141
 
5.3%
o 365546
 
5.1%
h 363859
 
5.1%
Other values (17) 1642361
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7108579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 874298
12.3%
a 736555
10.4%
668137
9.4%
i 617296
 
8.7%
t 593407
 
8.3%
n 470618
 
6.6%
e 397361
 
5.6%
r 379141
 
5.3%
o 365546
 
5.1%
h 363859
 
5.1%
Other values (17) 1642361
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7108579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 874298
12.3%
a 736555
10.4%
668137
9.4%
i 617296
 
8.7%
t 593407
 
8.3%
n 470618
 
6.6%
e 397361
 
5.6%
r 379141
 
5.3%
o 365546
 
5.1%
h 363859
 
5.1%
Other values (17) 1642361
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7108579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 874298
12.3%
a 736555
10.4%
668137
9.4%
i 617296
 
8.7%
t 593407
 
8.3%
n 470618
 
6.6%
e 397361
 
5.6%
r 379141
 
5.3%
o 365546
 
5.1%
h 363859
 
5.1%
Other values (17) 1642361
23.1%

waterBody
Text

Missing 

Distinct1778
Distinct (%)0.5%
Missing133831
Missing (%)29.3%
Memory size3.5 MiB
2025-03-26T16:29:06.863840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length72
Median length71
Mean length24.05877167
Min length6

Characters and Unicode

Total characters7778249
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique491 ?
Unique (%)0.2%

Sample

1st rowNorth Pacific Ocean
2nd rowNorth Atlantic Ocean, Gulf of Mexico
3rd rowNorth Pacific Ocean
4th rowAtlantic, Caribbean Sea
5th rowNorth Pacific Ocean
ValueCountFrequency (%)
ocean 297586
24.5%
north 201572
16.6%
pacific 178818
14.7%
atlantic 114202
 
9.4%
south 68354
 
5.6%
sea 63834
 
5.3%
of 34957
 
2.9%
gulf 34887
 
2.9%
bay 30221
 
2.5%
indian 28907
 
2.4%
Other values (1367) 159775
13.2%
2025-03-26T16:29:06.969059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
889811
11.4%
a 889529
11.4%
c 799399
 
10.3%
i 601025
 
7.7%
n 557711
 
7.2%
t 524730
 
6.7%
e 467110
 
6.0%
o 360903
 
4.6%
O 298509
 
3.8%
h 289425
 
3.7%
Other values (58) 2100097
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7778249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
889811
11.4%
a 889529
11.4%
c 799399
 
10.3%
i 601025
 
7.7%
n 557711
 
7.2%
t 524730
 
6.7%
e 467110
 
6.0%
o 360903
 
4.6%
O 298509
 
3.8%
h 289425
 
3.7%
Other values (58) 2100097
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7778249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
889811
11.4%
a 889529
11.4%
c 799399
 
10.3%
i 601025
 
7.7%
n 557711
 
7.2%
t 524730
 
6.7%
e 467110
 
6.0%
o 360903
 
4.6%
O 298509
 
3.8%
h 289425
 
3.7%
Other values (58) 2100097
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7778249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
889811
11.4%
a 889529
11.4%
c 799399
 
10.3%
i 601025
 
7.7%
n 557711
 
7.2%
t 524730
 
6.7%
e 467110
 
6.0%
o 360903
 
4.6%
O 298509
 
3.8%
h 289425
 
3.7%
Other values (58) 2100097
27.0%

islandGroup
Text

Missing 

Distinct324
Distinct (%)0.5%
Missing392466
Missing (%)85.9%
Memory size3.5 MiB
2025-03-26T16:29:07.095151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length32
Mean length14.8154391
Min length4

Characters and Unicode

Total characters958070
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.1%

Sample

1st rowFlorida Islands
2nd rowVava'u Group
3rd rowVisayas
4th rowCuyo Islands
5th rowHa'apai Group
ValueCountFrequency (%)
islands 31757
22.3%
group 14049
 
9.9%
chain 5505
 
3.9%
visayas 4965
 
3.5%
leeward 4840
 
3.4%
ralik 4627
 
3.2%
bahama 2881
 
2.0%
island 2815
 
2.0%
cruz 2218
 
1.6%
santa 2218
 
1.6%
Other values (355) 66553
46.7%
2025-03-26T16:29:07.275321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 144142
15.0%
s 92767
 
9.7%
77761
 
8.1%
n 71357
 
7.4%
l 57623
 
6.0%
d 51683
 
5.4%
r 46665
 
4.9%
u 38722
 
4.0%
o 37743
 
3.9%
i 37688
 
3.9%
Other values (54) 301919
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 958070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 144142
15.0%
s 92767
 
9.7%
77761
 
8.1%
n 71357
 
7.4%
l 57623
 
6.0%
d 51683
 
5.4%
r 46665
 
4.9%
u 38722
 
4.0%
o 37743
 
3.9%
i 37688
 
3.9%
Other values (54) 301919
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 958070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 144142
15.0%
s 92767
 
9.7%
77761
 
8.1%
n 71357
 
7.4%
l 57623
 
6.0%
d 51683
 
5.4%
r 46665
 
4.9%
u 38722
 
4.0%
o 37743
 
3.9%
i 37688
 
3.9%
Other values (54) 301919
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 958070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 144142
15.0%
s 92767
 
9.7%
77761
 
8.1%
n 71357
 
7.4%
l 57623
 
6.0%
d 51683
 
5.4%
r 46665
 
4.9%
u 38722
 
4.0%
o 37743
 
3.9%
i 37688
 
3.9%
Other values (54) 301919
31.5%

island
Text

Missing 

Distinct2224
Distinct (%)1.2%
Missing271745
Missing (%)59.4%
Memory size3.5 MiB
2025-03-26T16:29:07.406087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length37
Mean length9.782229702
Min length3

Characters and Unicode

Total characters1813508
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique461 ?
Unique (%)0.2%

Sample

1st rowHonshu
2nd rowBarbados
3rd rowPutic Island
4th rowGuam
5th rowFlorida Island
ValueCountFrequency (%)
island 45824
 
15.8%
bermuda 14579
 
5.0%
atoll 13153
 
4.5%
luzon 7667
 
2.6%
oahu 6820
 
2.3%
cay 5227
 
1.8%
carrie 3817
 
1.3%
bow 3817
 
1.3%
new 3023
 
1.0%
cuba 2710
 
0.9%
Other values (2080) 183713
63.3%
2025-03-26T16:29:07.681571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 270033
14.9%
n 134528
 
7.4%
o 117128
 
6.5%
l 111411
 
6.1%
104962
 
5.8%
u 91186
 
5.0%
e 88952
 
4.9%
i 86977
 
4.8%
r 86638
 
4.8%
d 84691
 
4.7%
Other values (70) 637002
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1813508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 270033
14.9%
n 134528
 
7.4%
o 117128
 
6.5%
l 111411
 
6.1%
104962
 
5.8%
u 91186
 
5.0%
e 88952
 
4.9%
i 86977
 
4.8%
r 86638
 
4.8%
d 84691
 
4.7%
Other values (70) 637002
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1813508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 270033
14.9%
n 134528
 
7.4%
o 117128
 
6.5%
l 111411
 
6.1%
104962
 
5.8%
u 91186
 
5.0%
e 88952
 
4.9%
i 86977
 
4.8%
r 86638
 
4.8%
d 84691
 
4.7%
Other values (70) 637002
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1813508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 270033
14.9%
n 134528
 
7.4%
o 117128
 
6.5%
l 111411
 
6.1%
104962
 
5.8%
u 91186
 
5.0%
e 88952
 
4.9%
i 86977
 
4.8%
r 86638
 
4.8%
d 84691
 
4.7%
Other values (70) 637002
35.1%

country
Text

Missing 

Distinct266
Distinct (%)0.1%
Missing36040
Missing (%)7.9%
Memory size3.5 MiB
2025-03-26T16:29:07.822912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length44
Median length32
Mean length10.33851429
Min length4

Characters and Unicode

Total characters4353476
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowJapan
4th rowUnited States
5th rowBarbados
ValueCountFrequency (%)
united 124517
19.9%
states 124274
19.9%
philippines 46328
 
7.4%
bermuda 15857
 
2.5%
islands 15777
 
2.5%
indonesia 12819
 
2.0%
brazil 11636
 
1.9%
french 10794
 
1.7%
new 10665
 
1.7%
panama 10486
 
1.7%
Other values (286) 242770
38.8%
2025-03-26T16:29:08.029883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 494092
11.3%
e 449914
 
10.3%
i 433705
 
10.0%
t 415767
 
9.6%
n 351553
 
8.1%
s 275149
 
6.3%
204830
 
4.7%
d 203972
 
4.7%
l 162326
 
3.7%
S 149605
 
3.4%
Other values (49) 1212563
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4353476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 494092
11.3%
e 449914
 
10.3%
i 433705
 
10.0%
t 415767
 
9.6%
n 351553
 
8.1%
s 275149
 
6.3%
204830
 
4.7%
d 203972
 
4.7%
l 162326
 
3.7%
S 149605
 
3.4%
Other values (49) 1212563
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4353476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 494092
11.3%
e 449914
 
10.3%
i 433705
 
10.0%
t 415767
 
9.6%
n 351553
 
8.1%
s 275149
 
6.3%
204830
 
4.7%
d 203972
 
4.7%
l 162326
 
3.7%
S 149605
 
3.4%
Other values (49) 1212563
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4353476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 494092
11.3%
e 449914
 
10.3%
i 433705
 
10.0%
t 415767
 
9.6%
n 351553
 
8.1%
s 275149
 
6.3%
204830
 
4.7%
d 203972
 
4.7%
l 162326
 
3.7%
S 149605
 
3.4%
Other values (49) 1212563
27.9%

stateProvince
Text

Missing 

Distinct1489
Distinct (%)0.5%
Missing175053
Missing (%)38.3%
Memory size3.5 MiB
2025-03-26T16:29:08.167058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length36
Mean length11.08306154
Min length3

Characters and Unicode

Total characters3126310
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique255 ?
Unique (%)0.1%

Sample

1st rowHawaii
2nd rowFlorida
3rd rowTokyo Prefecture
4th rowWest Virginia
5th rowPalawan
ValueCountFrequency (%)
province 31019
 
7.1%
florida 17264
 
4.0%
carolina 12578
 
2.9%
virginia 11546
 
2.7%
hawaii 10762
 
2.5%
north 9726
 
2.2%
region 9396
 
2.2%
south 8347
 
1.9%
maryland 7724
 
1.8%
islands 6738
 
1.5%
Other values (1481) 309677
71.2%
2025-03-26T16:29:08.367092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 412901
13.2%
i 267111
 
8.5%
n 235731
 
7.5%
o 225111
 
7.2%
e 217213
 
6.9%
r 215981
 
6.9%
152697
 
4.9%
s 137498
 
4.4%
t 133101
 
4.3%
l 124301
 
4.0%
Other values (87) 1004665
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3126310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 412901
13.2%
i 267111
 
8.5%
n 235731
 
7.5%
o 225111
 
7.2%
e 217213
 
6.9%
r 215981
 
6.9%
152697
 
4.9%
s 137498
 
4.4%
t 133101
 
4.3%
l 124301
 
4.0%
Other values (87) 1004665
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3126310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 412901
13.2%
i 267111
 
8.5%
n 235731
 
7.5%
o 225111
 
7.2%
e 217213
 
6.9%
r 215981
 
6.9%
152697
 
4.9%
s 137498
 
4.4%
t 133101
 
4.3%
l 124301
 
4.0%
Other values (87) 1004665
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3126310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 412901
13.2%
i 267111
 
8.5%
n 235731
 
7.5%
o 225111
 
7.2%
e 217213
 
6.9%
r 215981
 
6.9%
152697
 
4.9%
s 137498
 
4.4%
t 133101
 
4.3%
l 124301
 
4.0%
Other values (87) 1004665
32.1%

county
Text

Missing 

Distinct2320
Distinct (%)2.4%
Missing359033
Missing (%)78.5%
Memory size3.5 MiB
2025-03-26T16:29:08.511444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length40
Mean length14.85234455
Min length3

Characters and Unicode

Total characters1457015
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419 ?
Unique (%)0.4%

Sample

1st rowHillsborough County
2nd rowRandolph County
3rd rowThoothukudi District
4th rowCalvert County
5th rowNew Hanover County
ValueCountFrequency (%)
county 71959
34.9%
district 9154
 
4.4%
honolulu 5852
 
2.8%
monroe 3069
 
1.5%
parish 2208
 
1.1%
carteret 1949
 
0.9%
borough 1799
 
0.9%
san 1553
 
0.8%
montgomery 1352
 
0.7%
barnstable 1261
 
0.6%
Other values (2389) 105941
51.4%
2025-03-26T16:29:08.708328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 144638
 
9.9%
o 143249
 
9.8%
t 126559
 
8.7%
u 110945
 
7.6%
107997
 
7.4%
a 92505
 
6.3%
C 87227
 
6.0%
y 83923
 
5.8%
e 76187
 
5.2%
r 67309
 
4.6%
Other values (77) 416476
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1457015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 144638
 
9.9%
o 143249
 
9.8%
t 126559
 
8.7%
u 110945
 
7.6%
107997
 
7.4%
a 92505
 
6.3%
C 87227
 
6.0%
y 83923
 
5.8%
e 76187
 
5.2%
r 67309
 
4.6%
Other values (77) 416476
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1457015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 144638
 
9.9%
o 143249
 
9.8%
t 126559
 
8.7%
u 110945
 
7.6%
107997
 
7.4%
a 92505
 
6.3%
C 87227
 
6.0%
y 83923
 
5.8%
e 76187
 
5.2%
r 67309
 
4.6%
Other values (77) 416476
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1457015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 144638
 
9.9%
o 143249
 
9.8%
t 126559
 
8.7%
u 110945
 
7.6%
107997
 
7.4%
a 92505
 
6.3%
C 87227
 
6.0%
y 83923
 
5.8%
e 76187
 
5.2%
r 67309
 
4.6%
Other values (77) 416476
28.6%

locality
Text

Missing 

Distinct64099
Distinct (%)15.6%
Missing45318
Missing (%)9.9%
Memory size3.5 MiB
2025-03-26T16:29:08.856686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length653
Median length274
Mean length54.14236004
Min length1

Characters and Unicode

Total characters22296636
Distinct characters113
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31326 ?
Unique (%)7.6%

Sample

1st rowHawaii
2nd rowTampa, Florida
3rd rowTokyo, Japan
4th rowWest Virginia, Randolph County, Shaver's Fork at Cheat Bridge on US Route 250 (Durbin Quad)
5th rowNo Data
ValueCountFrequency (%)
of 177213
 
5.1%
island 102745
 
3.0%
islands 49176
 
1.4%
bay 45673
 
1.3%
river 43852
 
1.3%
reef 43265
 
1.2%
off 42353
 
1.2%
and 41211
 
1.2%
at 38855
 
1.1%
south 38481
 
1.1%
Other values (37147) 2842692
82.0%
2025-03-26T16:29:09.091390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3053701
 
13.7%
a 2157039
 
9.7%
e 1552103
 
7.0%
o 1449696
 
6.5%
n 1307438
 
5.9%
i 1148870
 
5.2%
t 1052551
 
4.7%
r 1052238
 
4.7%
s 992510
 
4.5%
l 841433
 
3.8%
Other values (103) 7689057
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22296636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3053701
 
13.7%
a 2157039
 
9.7%
e 1552103
 
7.0%
o 1449696
 
6.5%
n 1307438
 
5.9%
i 1148870
 
5.2%
t 1052551
 
4.7%
r 1052238
 
4.7%
s 992510
 
4.5%
l 841433
 
3.8%
Other values (103) 7689057
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22296636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3053701
 
13.7%
a 2157039
 
9.7%
e 1552103
 
7.0%
o 1449696
 
6.5%
n 1307438
 
5.9%
i 1148870
 
5.2%
t 1052551
 
4.7%
r 1052238
 
4.7%
s 992510
 
4.5%
l 841433
 
3.8%
Other values (103) 7689057
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22296636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3053701
 
13.7%
a 2157039
 
9.7%
e 1552103
 
7.0%
o 1449696
 
6.5%
n 1307438
 
5.9%
i 1148870
 
5.2%
t 1052551
 
4.7%
r 1052238
 
4.7%
s 992510
 
4.5%
l 841433
 
3.8%
Other values (103) 7689057
34.5%

verbatimElevation
Text

Missing 

Distinct76
Distinct (%)3.4%
Missing454919
Missing (%)99.5%
Memory size3.5 MiB
2025-03-26T16:29:09.194511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length152
Median length68
Mean length46.43495935
Min length3

Characters and Unicode

Total characters102807
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.6%

Sample

1st rowRotenone put out at 90' and 120', pickup was surface to 140', several (fiscos=factors?) prevented an even better collection.
2nd rowDistance from shore: 1000 feet
3rd row32 not found in field notes so could be inaccurate.
4th rowDistance from shore: 1500 feet
5th rowNaso was speared by P.W. (Paul D. West)
ValueCountFrequency (%)
feet 1689
 
9.0%
distance 1145
 
6.1%
from 1101
 
5.8%
to 1074
 
5.7%
shore 1052
 
5.6%
at 599
 
3.2%
500
 
2.7%
and 449
 
2.4%
rotenone 432
 
2.3%
put 311
 
1.7%
Other values (175) 10488
55.7%
2025-03-26T16:29:09.360184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16626
16.2%
e 10316
 
10.0%
t 7406
 
7.2%
o 6848
 
6.7%
a 5096
 
5.0%
s 4817
 
4.7%
f 4225
 
4.1%
n 4168
 
4.1%
r 4120
 
4.0%
0 3369
 
3.3%
Other values (60) 35816
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16626
16.2%
e 10316
 
10.0%
t 7406
 
7.2%
o 6848
 
6.7%
a 5096
 
5.0%
s 4817
 
4.7%
f 4225
 
4.1%
n 4168
 
4.1%
r 4120
 
4.0%
0 3369
 
3.3%
Other values (60) 35816
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16626
16.2%
e 10316
 
10.0%
t 7406
 
7.2%
o 6848
 
6.7%
a 5096
 
5.0%
s 4817
 
4.7%
f 4225
 
4.1%
n 4168
 
4.1%
r 4120
 
4.0%
0 3369
 
3.3%
Other values (60) 35816
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16626
16.2%
e 10316
 
10.0%
t 7406
 
7.2%
o 6848
 
6.7%
a 5096
 
5.0%
s 4817
 
4.7%
f 4225
 
4.1%
n 4168
 
4.1%
r 4120
 
4.0%
0 3369
 
3.3%
Other values (60) 35816
34.8%

minimumDepthInMeters
Text

Missing 

Distinct1924
Distinct (%)0.9%
Missing250216
Missing (%)54.7%
Memory size3.5 MiB
2025-03-26T16:29:09.486625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.591130743
Min length3

Characters and Unicode

Total characters743066
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)0.2%

Sample

1st row60.0
2nd row0.0
3rd row37.0
4th row7.0
5th row2.0
ValueCountFrequency (%)
0.0 76225
36.8%
2.0 10630
 
5.1%
1.0 9015
 
4.4%
3.0 6955
 
3.4%
5.0 4114
 
2.0%
4.0 3224
 
1.6%
9.0 2705
 
1.3%
6.0 2677
 
1.3%
18.0 2647
 
1.3%
15.0 2576
 
1.2%
Other values (1914) 86149
41.6%
2025-03-26T16:29:09.675204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 305145
41.1%
. 206917
27.8%
1 50241
 
6.8%
2 41011
 
5.5%
5 26986
 
3.6%
3 26917
 
3.6%
4 21274
 
2.9%
6 17842
 
2.4%
8 16452
 
2.2%
7 15542
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 305145
41.1%
. 206917
27.8%
1 50241
 
6.8%
2 41011
 
5.5%
5 26986
 
3.6%
3 26917
 
3.6%
4 21274
 
2.9%
6 17842
 
2.4%
8 16452
 
2.2%
7 15542
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 305145
41.1%
. 206917
27.8%
1 50241
 
6.8%
2 41011
 
5.5%
5 26986
 
3.6%
3 26917
 
3.6%
4 21274
 
2.9%
6 17842
 
2.4%
8 16452
 
2.2%
7 15542
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 305145
41.1%
. 206917
27.8%
1 50241
 
6.8%
2 41011
 
5.5%
5 26986
 
3.6%
3 26917
 
3.6%
4 21274
 
2.9%
6 17842
 
2.4%
8 16452
 
2.2%
7 15542
 
2.1%

maximumDepthInMeters
Text

Missing 

Distinct2021
Distinct (%)1.1%
Missing265019
Missing (%)58.0%
Memory size3.5 MiB
2025-03-26T16:29:09.804279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.805089686
Min length3

Characters and Unicode

Total characters731011
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique364 ?
Unique (%)0.2%

Sample

1st row39.0
2nd row4.6
3rd row46.0
4th row8.0
5th row5.0
ValueCountFrequency (%)
1.0 16685
 
8.7%
2.0 10804
 
5.6%
5.0 9987
 
5.2%
3.0 8398
 
4.4%
6.0 7378
 
3.8%
0.0 5350
 
2.8%
8.0 4976
 
2.6%
9.0 4755
 
2.5%
4.0 4730
 
2.5%
12.0 4136
 
2.2%
Other values (2011) 114915
59.8%
2025-03-26T16:29:09.994799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 228715
31.3%
. 192114
26.3%
1 75433
 
10.3%
2 49217
 
6.7%
5 41885
 
5.7%
3 35692
 
4.9%
4 24508
 
3.4%
6 23931
 
3.3%
8 22294
 
3.0%
7 19100
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 731011
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 228715
31.3%
. 192114
26.3%
1 75433
 
10.3%
2 49217
 
6.7%
5 41885
 
5.7%
3 35692
 
4.9%
4 24508
 
3.4%
6 23931
 
3.3%
8 22294
 
3.0%
7 19100
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 731011
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 228715
31.3%
. 192114
26.3%
1 75433
 
10.3%
2 49217
 
6.7%
5 41885
 
5.7%
3 35692
 
4.9%
4 24508
 
3.4%
6 23931
 
3.3%
8 22294
 
3.0%
7 19100
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 731011
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 228715
31.3%
. 192114
26.3%
1 75433
 
10.3%
2 49217
 
6.7%
5 41885
 
5.7%
3 35692
 
4.9%
4 24508
 
3.4%
6 23931
 
3.3%
8 22294
 
3.0%
7 19100
 
2.6%

verbatimDepth
Text

Missing 

Distinct231
Distinct (%)2.7%
Missing448526
Missing (%)98.1%
Memory size3.5 MiB
2025-03-26T16:29:10.126657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length72
Median length67
Mean length8.248751017
Min length1

Characters and Unicode

Total characters70997
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)1.1%

Sample

1st rowDepth trawl: 135 fathoms
2nd row15 minutes at depth
3rd rowSurface
4th rowCA
5th row15 minutes at depth
ValueCountFrequency (%)
ca 3946
25.9%
surface 2356
15.5%
depth 870
 
5.7%
at 577
 
3.8%
00000000 545
 
3.6%
to 506
 
3.3%
minutes 347
 
2.3%
fathoms 330
 
2.2%
m 322
 
2.1%
trawl 287
 
1.9%
Other values (306) 5131
33.7%
2025-03-26T16:29:10.329451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7699
 
10.8%
6610
 
9.3%
e 5157
 
7.3%
a 4486
 
6.3%
t 4142
 
5.8%
A 4121
 
5.8%
C 3965
 
5.6%
r 3476
 
4.9%
f 3208
 
4.5%
u 3144
 
4.4%
Other values (66) 24989
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7699
 
10.8%
6610
 
9.3%
e 5157
 
7.3%
a 4486
 
6.3%
t 4142
 
5.8%
A 4121
 
5.8%
C 3965
 
5.6%
r 3476
 
4.9%
f 3208
 
4.5%
u 3144
 
4.4%
Other values (66) 24989
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7699
 
10.8%
6610
 
9.3%
e 5157
 
7.3%
a 4486
 
6.3%
t 4142
 
5.8%
A 4121
 
5.8%
C 3965
 
5.6%
r 3476
 
4.9%
f 3208
 
4.5%
u 3144
 
4.4%
Other values (66) 24989
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7699
 
10.8%
6610
 
9.3%
e 5157
 
7.3%
a 4486
 
6.3%
t 4142
 
5.8%
A 4121
 
5.8%
C 3965
 
5.6%
r 3476
 
4.9%
f 3208
 
4.5%
u 3144
 
4.4%
Other values (66) 24989
35.2%

decimalLatitude
Text

Missing 

Distinct15650
Distinct (%)7.8%
Missing255392
Missing (%)55.9%
Memory size3.5 MiB
2025-03-26T16:29:10.464428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.999985129
Min length3

Characters and Unicode

Total characters1210443
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4361 ?
Unique (%)2.2%

Sample

1st row13.2431
2nd row10.9181
3rd row31.93
4th row10.72
5th row-2.0517
ValueCountFrequency (%)
12.5 1214
 
0.6%
27.9 873
 
0.4%
16.8 715
 
0.4%
12.0832 625
 
0.3%
21.417 546
 
0.3%
19.1606 545
 
0.3%
32.23 515
 
0.3%
32.17 503
 
0.2%
32.3 495
 
0.2%
28.4933 489
 
0.2%
Other values (14234) 195221
96.8%
2025-03-26T16:29:10.662324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 201741
16.7%
3 140719
11.6%
1 140202
11.6%
2 127641
10.5%
8 92659
7.7%
7 92095
7.6%
5 85028
7.0%
4 71361
 
5.9%
6 70097
 
5.8%
9 69832
 
5.8%
Other values (2) 119068
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1210443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 201741
16.7%
3 140719
11.6%
1 140202
11.6%
2 127641
10.5%
8 92659
7.7%
7 92095
7.6%
5 85028
7.0%
4 71361
 
5.9%
6 70097
 
5.8%
9 69832
 
5.8%
Other values (2) 119068
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1210443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 201741
16.7%
3 140719
11.6%
1 140202
11.6%
2 127641
10.5%
8 92659
7.7%
7 92095
7.6%
5 85028
7.0%
4 71361
 
5.9%
6 70097
 
5.8%
9 69832
 
5.8%
Other values (2) 119068
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1210443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 201741
16.7%
3 140719
11.6%
1 140202
11.6%
2 127641
10.5%
8 92659
7.7%
7 92095
7.6%
5 85028
7.0%
4 71361
 
5.9%
6 70097
 
5.8%
9 69832
 
5.8%
Other values (2) 119068
9.8%

decimalLongitude
Text

Missing 

Distinct17160
Distinct (%)8.5%
Missing255392
Missing (%)55.9%
Memory size3.5 MiB
2025-03-26T16:29:10.812674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.717414903
Min length3

Characters and Unicode

Total characters1355178
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5091 ?
Unique (%)2.5%

Sample

1st row-59.6561
2nd row121.034
3rd row-63.95
4th row-67.88
5th row130.107
ValueCountFrequency (%)
177.083 873
 
0.4%
93.717 820
 
0.4%
88.08 741
 
0.4%
68.8991 623
 
0.3%
64.0 568
 
0.3%
158.417 547
 
0.3%
179.756 545
 
0.3%
162.875 491
 
0.2%
165.83 473
 
0.2%
84.9317 454
 
0.2%
Other values (16321) 195606
97.0%
2025-03-26T16:29:11.016483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 201741
14.9%
1 165292
12.2%
7 129582
9.6%
- 126179
9.3%
8 119886
8.8%
3 100500
7.4%
6 100258
7.4%
2 98317
7.3%
5 93721
6.9%
4 79477
 
5.9%
Other values (2) 140225
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1355178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 201741
14.9%
1 165292
12.2%
7 129582
9.6%
- 126179
9.3%
8 119886
8.8%
3 100500
7.4%
6 100258
7.4%
2 98317
7.3%
5 93721
6.9%
4 79477
 
5.9%
Other values (2) 140225
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1355178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 201741
14.9%
1 165292
12.2%
7 129582
9.6%
- 126179
9.3%
8 119886
8.8%
3 100500
7.4%
6 100258
7.4%
2 98317
7.3%
5 93721
6.9%
4 79477
 
5.9%
Other values (2) 140225
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1355178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 201741
14.9%
1 165292
12.2%
7 129582
9.6%
- 126179
9.3%
8 119886
8.8%
3 100500
7.4%
6 100258
7.4%
2 98317
7.3%
5 93721
6.9%
4 79477
 
5.9%
Other values (2) 140225
10.3%

geodeticDatum
Text

Missing 

Distinct5
Distinct (%)0.1%
Missing449927
Missing (%)98.4%
Memory size3.5 MiB
2025-03-26T16:29:11.063597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length18
Mean length17.98945323
Min length5

Characters and Unicode

Total characters129632
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWGS 84 (EPSG:4326)
2nd rowWGS 84 (EPSG:4326)
3rd rowWGS 84 (EPSG:4326)
4th rowWGS 84 (EPSG:4326)
5th rowWGS 84 (EPSG:4326)
ValueCountFrequency (%)
wgs 7104
32.9%
84 7104
32.9%
epsg:4326 7054
32.7%
nad83 69
 
0.3%
epsg:4269 69
 
0.3%
epsg 50
 
0.2%
4326 50
 
0.2%
nad27 31
 
0.1%
epsg:4267 31
 
0.1%
wgs84 2
 
< 0.1%
2025-03-26T16:29:11.150485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14358
11.1%
S 14310
11.0%
4 14310
11.0%
G 14310
11.0%
2 7235
 
5.6%
: 7204
 
5.6%
) 7204
 
5.6%
( 7204
 
5.6%
E 7204
 
5.6%
P 7204
 
5.6%
Other values (9) 29089
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14358
11.1%
S 14310
11.0%
4 14310
11.0%
G 14310
11.0%
2 7235
 
5.6%
: 7204
 
5.6%
) 7204
 
5.6%
( 7204
 
5.6%
E 7204
 
5.6%
P 7204
 
5.6%
Other values (9) 29089
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14358
11.1%
S 14310
11.0%
4 14310
11.0%
G 14310
11.0%
2 7235
 
5.6%
: 7204
 
5.6%
) 7204
 
5.6%
( 7204
 
5.6%
E 7204
 
5.6%
P 7204
 
5.6%
Other values (9) 29089
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14358
11.1%
S 14310
11.0%
4 14310
11.0%
G 14310
11.0%
2 7235
 
5.6%
: 7204
 
5.6%
) 7204
 
5.6%
( 7204
 
5.6%
E 7204
 
5.6%
P 7204
 
5.6%
Other values (9) 29089
22.4%
Distinct220
Distinct (%)4.2%
Missing451954
Missing (%)98.9%
Memory size3.5 MiB
2025-03-26T16:29:11.255134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.780845723
Min length2

Characters and Unicode

Total characters19581
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.7%

Sample

1st row100
2nd row457
3rd row739
4th row100
5th row8438
ValueCountFrequency (%)
100 1117
21.6%
10000 837
 
16.2%
3704 209
 
4.0%
500 189
 
3.6%
5000 122
 
2.4%
278076 107
 
2.1%
441 90
 
1.7%
330 83
 
1.6%
50 78
 
1.5%
161 73
 
1.4%
Other values (210) 2274
43.9%
2025-03-26T16:29:11.413275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8047
41.1%
1 3250
16.6%
2 1525
 
7.8%
4 1398
 
7.1%
3 1200
 
6.1%
5 1168
 
6.0%
6 829
 
4.2%
7 778
 
4.0%
8 723
 
3.7%
9 636
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8047
41.1%
1 3250
16.6%
2 1525
 
7.8%
4 1398
 
7.1%
3 1200
 
6.1%
5 1168
 
6.0%
6 829
 
4.2%
7 778
 
4.0%
8 723
 
3.7%
9 636
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8047
41.1%
1 3250
16.6%
2 1525
 
7.8%
4 1398
 
7.1%
3 1200
 
6.1%
5 1168
 
6.0%
6 829
 
4.2%
7 778
 
4.0%
8 723
 
3.7%
9 636
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8047
41.1%
1 3250
16.6%
2 1525
 
7.8%
4 1398
 
7.1%
3 1200
 
6.1%
5 1168
 
6.0%
6 829
 
4.2%
7 778
 
4.0%
8 723
 
3.7%
9 636
 
3.2%

verbatimLatitude
Text

Missing 

Distinct14126
Distinct (%)7.8%
Missing276490
Missing (%)60.5%
Memory size3.5 MiB
2025-03-26T16:29:11.548795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length39
Mean length7.520817303
Min length2

Characters and Unicode

Total characters1358583
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3894 ?
Unique (%)2.2%

Sample

1st row13.243 N
2nd row105505N
3rd row3156 N
4th row1043 N
5th row020306S
ValueCountFrequency (%)
n 85213
 
24.0%
s 26456
 
7.5%
00 4852
 
1.4%
28 4352
 
1.2%
30 4238
 
1.2%
21 2920
 
0.8%
27 2439
 
0.7%
13 2228
 
0.6%
12 2206
 
0.6%
25 2015
 
0.6%
Other values (10729) 218135
61.4%
2025-03-26T16:29:11.823703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 175773
12.9%
174411
12.8%
1 150072
11.0%
2 132037
9.7%
3 130670
9.6%
N 123787
9.1%
4 91098
6.7%
5 88888
6.5%
8 56731
 
4.2%
9 52036
 
3.8%
Other values (27) 183080
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1358583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175773
12.9%
174411
12.8%
1 150072
11.0%
2 132037
9.7%
3 130670
9.6%
N 123787
9.1%
4 91098
6.7%
5 88888
6.5%
8 56731
 
4.2%
9 52036
 
3.8%
Other values (27) 183080
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1358583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175773
12.9%
174411
12.8%
1 150072
11.0%
2 132037
9.7%
3 130670
9.6%
N 123787
9.1%
4 91098
6.7%
5 88888
6.5%
8 56731
 
4.2%
9 52036
 
3.8%
Other values (27) 183080
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1358583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175773
12.9%
174411
12.8%
1 150072
11.0%
2 132037
9.7%
3 130670
9.6%
N 123787
9.1%
4 91098
6.7%
5 88888
6.5%
8 56731
 
4.2%
9 52036
 
3.8%
Other values (27) 183080
13.5%

verbatimLongitude
Text

Missing 

Distinct15229
Distinct (%)8.4%
Missing276551
Missing (%)60.5%
Memory size3.5 MiB
2025-03-26T16:29:11.859560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length49
Median length46
Mean length8.464697478
Min length2

Characters and Unicode

Total characters1528572
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4422 ?
Unique (%)2.4%

Sample

1st row-59.656 W
2nd row1210203E
3rd row06357 W
4th row06753 W
5th row1300624E
ValueCountFrequency (%)
w 80459
 
22.7%
e 30807
 
8.7%
00 4572
 
1.3%
30 3574
 
1.0%
88 2948
 
0.8%
85 1705
 
0.5%
120 1690
 
0.5%
1400230e 1503
 
0.4%
121 1393
 
0.4%
77 1353
 
0.4%
Other values (12442) 224766
63.4%
2025-03-26T16:29:11.957568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 225193
14.7%
174188
11.4%
1 167116
10.9%
5 116605
7.6%
2 109057
7.1%
W 107529
7.0%
4 102490
6.7%
3 99556
 
6.5%
7 86431
 
5.7%
6 84589
 
5.5%
Other values (25) 255818
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1528572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 225193
14.7%
174188
11.4%
1 167116
10.9%
5 116605
7.6%
2 109057
7.1%
W 107529
7.0%
4 102490
6.7%
3 99556
 
6.5%
7 86431
 
5.7%
6 84589
 
5.5%
Other values (25) 255818
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1528572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 225193
14.7%
174188
11.4%
1 167116
10.9%
5 116605
7.6%
2 109057
7.1%
W 107529
7.0%
4 102490
6.7%
3 99556
 
6.5%
7 86431
 
5.7%
6 84589
 
5.5%
Other values (25) 255818
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1528572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 225193
14.7%
174188
11.4%
1 167116
10.9%
5 116605
7.6%
2 109057
7.1%
W 107529
7.0%
4 102490
6.7%
3 99556
 
6.5%
7 86431
 
5.7%
6 84589
 
5.5%
Other values (25) 255818
16.7%
Distinct3
Distinct (%)< 0.1%
Missing310239
Missing (%)67.9%
Memory size3.5 MiB
2025-03-26T16:29:11.985570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.92756682
Min length7

Characters and Unicode

Total characters3367922
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 146886
33.4%
minutes 145572
33.1%
seconds 145572
33.1%
decimal 1314
 
0.3%
unknown 8
 
< 0.1%
2025-03-26T16:29:12.071612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 733116
21.8%
s 438030
13.0%
292458
 
8.7%
n 291168
 
8.6%
D 146886
 
4.4%
c 146886
 
4.4%
g 146886
 
4.4%
r 146886
 
4.4%
d 146886
 
4.4%
i 146886
 
4.4%
Other values (11) 731834
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3367922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 733116
21.8%
s 438030
13.0%
292458
 
8.7%
n 291168
 
8.6%
D 146886
 
4.4%
c 146886
 
4.4%
g 146886
 
4.4%
r 146886
 
4.4%
d 146886
 
4.4%
i 146886
 
4.4%
Other values (11) 731834
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3367922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 733116
21.8%
s 438030
13.0%
292458
 
8.7%
n 291168
 
8.6%
D 146886
 
4.4%
c 146886
 
4.4%
g 146886
 
4.4%
r 146886
 
4.4%
d 146886
 
4.4%
i 146886
 
4.4%
Other values (11) 731834
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3367922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 733116
21.8%
s 438030
13.0%
292458
 
8.7%
n 291168
 
8.6%
D 146886
 
4.4%
c 146886
 
4.4%
g 146886
 
4.4%
r 146886
 
4.4%
d 146886
 
4.4%
i 146886
 
4.4%
Other values (11) 731834
21.7%

georeferenceProtocol
Text

Missing 

Distinct16
Distinct (%)0.1%
Missing439678
Missing (%)96.2%
Memory size3.5 MiB
2025-03-26T16:29:12.110617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length125
Median length96
Mean length19.26387854
Min length3

Characters and Unicode

Total characters336251
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGPS
2nd rowOn-line Gazetteer
3rd rowDifferential GPS
4th rowGuide to Best Practices for Georeferencing. (Chapman and Wieczorek, eds. 2006). Google Earth Pro
5th rowChart
ValueCountFrequency (%)
chart 6356
 
11.9%
gps 6356
 
11.9%
google 3640
 
6.8%
earth 3269
 
6.1%
georeferencing 2460
 
4.6%
and 2437
 
4.6%
pro 2410
 
4.5%
2006 2410
 
4.5%
wieczorek 2410
 
4.5%
eds 2410
 
4.5%
Other values (37) 19321
36.1%
2025-03-26T16:29:12.208955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36024
 
10.7%
e 34162
 
10.2%
r 26812
 
8.0%
a 22084
 
6.6%
t 20314
 
6.0%
o 19940
 
5.9%
G 16423
 
4.9%
n 13503
 
4.0%
h 12595
 
3.7%
i 12219
 
3.6%
Other values (51) 122175
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 336251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36024
 
10.7%
e 34162
 
10.2%
r 26812
 
8.0%
a 22084
 
6.6%
t 20314
 
6.0%
o 19940
 
5.9%
G 16423
 
4.9%
n 13503
 
4.0%
h 12595
 
3.7%
i 12219
 
3.6%
Other values (51) 122175
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 336251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36024
 
10.7%
e 34162
 
10.2%
r 26812
 
8.0%
a 22084
 
6.6%
t 20314
 
6.0%
o 19940
 
5.9%
G 16423
 
4.9%
n 13503
 
4.0%
h 12595
 
3.7%
i 12219
 
3.6%
Other values (51) 122175
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 336251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36024
 
10.7%
e 34162
 
10.2%
r 26812
 
8.0%
a 22084
 
6.6%
t 20314
 
6.0%
o 19940
 
5.9%
G 16423
 
4.9%
n 13503
 
4.0%
h 12595
 
3.7%
i 12219
 
3.6%
Other values (51) 122175
36.3%

georeferenceRemarks
Text

Missing 

Distinct135
Distinct (%)0.6%
Missing434034
Missing (%)94.9%
Memory size3.5 MiB
2025-03-26T16:29:12.247368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length158
Median length2
Mean length7.227542318
Min length1

Characters and Unicode

Total characters166949
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)0.3%

Sample

1st rowStart; End
2nd rowca
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 18480
46.3%
start 2531
 
6.3%
end 2437
 
6.1%
bank 1778
 
4.5%
flower 1778
 
4.5%
garden 1778
 
4.5%
for 981
 
2.5%
west 945
 
2.4%
east 833
 
2.1%
coordinates 582
 
1.5%
Other values (263) 7824
19.6%
2025-03-26T16:29:12.357556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 17165
 
10.3%
16848
 
10.1%
A 16607
 
9.9%
a 12145
 
7.3%
t 11600
 
6.9%
n 10380
 
6.2%
e 9956
 
6.0%
r 8739
 
5.2%
o 7920
 
4.7%
d 6250
 
3.7%
Other values (50) 49339
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 17165
 
10.3%
16848
 
10.1%
A 16607
 
9.9%
a 12145
 
7.3%
t 11600
 
6.9%
n 10380
 
6.2%
e 9956
 
6.0%
r 8739
 
5.2%
o 7920
 
4.7%
d 6250
 
3.7%
Other values (50) 49339
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 17165
 
10.3%
16848
 
10.1%
A 16607
 
9.9%
a 12145
 
7.3%
t 11600
 
6.9%
n 10380
 
6.2%
e 9956
 
6.0%
r 8739
 
5.2%
o 7920
 
4.7%
d 6250
 
3.7%
Other values (50) 49339
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 17165
 
10.3%
16848
 
10.1%
A 16607
 
9.9%
a 12145
 
7.3%
t 11600
 
6.9%
n 10380
 
6.2%
e 9956
 
6.0%
r 8739
 
5.2%
o 7920
 
4.7%
d 6250
 
3.7%
Other values (50) 49339
29.6%
Distinct5
Distinct (%)0.3%
Missing455433
Missing (%)99.6%
Memory size3.5 MiB
2025-03-26T16:29:12.386556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length3
Mean length5.774117647
Min length3

Characters and Unicode

Total characters9816
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcf.
2nd rowuncertain
3rd rowuncertain
4th rowuncertain
5th rownear
ValueCountFrequency (%)
cf 899
52.9%
uncertain 783
46.1%
aff 14
 
0.8%
near 4
 
0.2%
2025-03-26T16:29:12.465822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 1682
17.1%
n 1570
16.0%
f 927
9.4%
. 913
9.3%
a 801
8.2%
e 787
8.0%
r 787
8.0%
t 783
8.0%
i 783
8.0%
u 652
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 1682
17.1%
n 1570
16.0%
f 927
9.4%
. 913
9.3%
a 801
8.2%
e 787
8.0%
r 787
8.0%
t 783
8.0%
i 783
8.0%
u 652
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 1682
17.1%
n 1570
16.0%
f 927
9.4%
. 913
9.3%
a 801
8.2%
e 787
8.0%
r 787
8.0%
t 783
8.0%
i 783
8.0%
u 652
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 1682
17.1%
n 1570
16.0%
f 927
9.4%
. 913
9.3%
a 801
8.2%
e 787
8.0%
r 787
8.0%
t 783
8.0%
i 783
8.0%
u 652
 
6.6%

typeStatus
Text

Missing 

Distinct48
Distinct (%)0.2%
Missing437569
Missing (%)95.7%
Memory size3.5 MiB
2025-03-26T16:29:12.494823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length8
Mean length8.127887957
Min length2

Characters and Unicode

Total characters159014
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st rowParatype
2nd rowHolotype
3rd rowParalectotype; Syntype
4th rowParatype
5th rowParatype
ValueCountFrequency (%)
paratype 12526
61.7%
holotype 3372
 
16.6%
type 1504
 
7.4%
syntype 1445
 
7.1%
paralectotype 656
 
3.2%
lectotype 331
 
1.6%
cotype 311
 
1.5%
neotype 69
 
0.3%
ms 39
 
0.2%
unconfirmed 16
 
0.1%
Other values (2) 18
 
0.1%
2025-03-26T16:29:12.586370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 26380
16.6%
y 21677
13.6%
e 21304
13.4%
p 20240
12.7%
t 19723
12.4%
r 13206
8.3%
P 13190
8.3%
o 8153
 
5.1%
l 4048
 
2.5%
H 3372
 
2.1%
Other values (17) 7721
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 26380
16.6%
y 21677
13.6%
e 21304
13.4%
p 20240
12.7%
t 19723
12.4%
r 13206
8.3%
P 13190
8.3%
o 8153
 
5.1%
l 4048
 
2.5%
H 3372
 
2.1%
Other values (17) 7721
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 26380
16.6%
y 21677
13.6%
e 21304
13.4%
p 20240
12.7%
t 19723
12.4%
r 13206
8.3%
P 13190
8.3%
o 8153
 
5.1%
l 4048
 
2.5%
H 3372
 
2.1%
Other values (17) 7721
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 26380
16.6%
y 21677
13.6%
e 21304
13.4%
p 20240
12.7%
t 19723
12.4%
r 13206
8.3%
P 13190
8.3%
o 8153
 
5.1%
l 4048
 
2.5%
H 3372
 
2.1%
Other values (17) 7721
 
4.9%

identifiedBy
Text

Missing 

Distinct573
Distinct (%)1.7%
Missing422838
Missing (%)92.5%
Memory size3.5 MiB
2025-03-26T16:29:12.718435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length147
Median length137
Mean length21.13800846
Min length5

Characters and Unicode

Total characters724928
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)0.4%

Sample

1st rowPezold, Frank; Larson, Helen K.
2nd rowWilliams, Jeffrey T.
3rd rowWilliams, Jeffrey T.
4th rowEschmeyer, William N.
5th rowKarnella, Susan J.
ValueCountFrequency (%)
williams 6522
 
5.8%
jeffrey 6391
 
5.7%
t 6390
 
5.7%
e 4392
 
3.9%
david 4233
 
3.8%
g 4067
 
3.6%
smith 3805
 
3.4%
c 2669
 
2.4%
pitassy 2535
 
2.3%
diane 2535
 
2.3%
Other values (968) 68764
61.2%
2025-03-26T16:29:12.939425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78008
 
10.8%
a 55493
 
7.7%
i 54286
 
7.5%
e 50434
 
7.0%
, 37979
 
5.2%
r 34179
 
4.7%
l 32237
 
4.4%
n 30962
 
4.3%
. 27043
 
3.7%
t 26973
 
3.7%
Other values (59) 297334
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 724928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
78008
 
10.8%
a 55493
 
7.7%
i 54286
 
7.5%
e 50434
 
7.0%
, 37979
 
5.2%
r 34179
 
4.7%
l 32237
 
4.4%
n 30962
 
4.3%
. 27043
 
3.7%
t 26973
 
3.7%
Other values (59) 297334
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 724928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
78008
 
10.8%
a 55493
 
7.7%
i 54286
 
7.5%
e 50434
 
7.0%
, 37979
 
5.2%
r 34179
 
4.7%
l 32237
 
4.4%
n 30962
 
4.3%
. 27043
 
3.7%
t 26973
 
3.7%
Other values (59) 297334
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 724928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
78008
 
10.8%
a 55493
 
7.7%
i 54286
 
7.5%
e 50434
 
7.0%
, 37979
 
5.2%
r 34179
 
4.7%
l 32237
 
4.4%
n 30962
 
4.3%
. 27043
 
3.7%
t 26973
 
3.7%
Other values (59) 297334
41.0%
Distinct30246
Distinct (%)6.6%
Missing7
Missing (%)< 0.1%
Memory size3.5 MiB
2025-03-26T16:29:13.059800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length69
Median length54
Mean length18.57321832
Min length2

Characters and Unicode

Total characters8490301
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9337 ?
Unique (%)2.0%

Sample

1st rowEchidna nebulosa
2nd rowMugil
3rd rowCryptocentrus filifer
4th rowRhinichthys cataractae
5th rowCentropomus ensiferus
ValueCountFrequency (%)
notropis 7227
 
0.8%
etheostoma 4913
 
0.6%
chaetodon 4356
 
0.5%
gymnothorax 4347
 
0.5%
lepomis 4296
 
0.5%
lutjanus 3911
 
0.5%
chromis 3164
 
0.4%
halichoeres 3141
 
0.4%
pomacentrus 2969
 
0.3%
acanthurus 2908
 
0.3%
Other values (18901) 817369
95.2%
2025-03-26T16:29:13.261712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 776558
 
9.1%
a 772604
 
9.1%
i 703809
 
8.3%
o 621580
 
7.3%
e 561867
 
6.6%
u 537716
 
6.3%
r 510572
 
6.0%
t 481190
 
5.7%
n 448155
 
5.3%
401475
 
4.7%
Other values (60) 2674775
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8490301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 776558
 
9.1%
a 772604
 
9.1%
i 703809
 
8.3%
o 621580
 
7.3%
e 561867
 
6.6%
u 537716
 
6.3%
r 510572
 
6.0%
t 481190
 
5.7%
n 448155
 
5.3%
401475
 
4.7%
Other values (60) 2674775
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8490301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 776558
 
9.1%
a 772604
 
9.1%
i 703809
 
8.3%
o 621580
 
7.3%
e 561867
 
6.6%
u 537716
 
6.3%
r 510572
 
6.0%
t 481190
 
5.7%
n 448155
 
5.3%
401475
 
4.7%
Other values (60) 2674775
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8490301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 776558
 
9.1%
a 772604
 
9.1%
i 703809
 
8.3%
o 621580
 
7.3%
e 561867
 
6.6%
u 537716
 
6.3%
r 510572
 
6.0%
t 481190
 
5.7%
n 448155
 
5.3%
401475
 
4.7%
Other values (60) 2674775
31.5%
Distinct863
Distinct (%)0.2%
Missing232
Missing (%)0.1%
Memory size3.5 MiB
2025-03-26T16:29:13.385078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length164
Median length155
Mean length131.5155887
Min length8

Characters and Unicode

Total characters60089604
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Elopomorpha, Anguilliformes, Muraenoidei, Muraenidae, Muraeninae
2nd rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Percoidei, Mugilidae
3rd rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Gobioidei, Gobiidae, Gobiinae
4th rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Ostariophysi, Cypriniformes, Cyprinidae
5th rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Percoidei, Centropomidae
ValueCountFrequency (%)
chordata 456892
 
9.9%
animalia 456848
 
9.9%
vertebrata 456333
 
9.8%
osteichthyes 446393
 
9.6%
actinopterygii 446336
 
9.6%
neopterygii 445902
 
9.6%
acanthopterygii 294290
 
6.4%
perciformes 214688
 
4.6%
percoidei 97321
 
2.1%
ostariophysi 67849
 
1.5%
Other values (969) 1251386
27.0%
2025-03-26T16:29:13.583558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6718917
 
11.2%
e 5788992
 
9.6%
t 4882790
 
8.1%
a 4472087
 
7.4%
, 4177337
 
7.0%
4177337
 
7.0%
r 4173639
 
6.9%
o 3451884
 
5.7%
h 2166498
 
3.6%
n 2110119
 
3.5%
Other values (41) 17970004
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60089604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6718917
 
11.2%
e 5788992
 
9.6%
t 4882790
 
8.1%
a 4472087
 
7.4%
, 4177337
 
7.0%
4177337
 
7.0%
r 4173639
 
6.9%
o 3451884
 
5.7%
h 2166498
 
3.6%
n 2110119
 
3.5%
Other values (41) 17970004
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60089604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6718917
 
11.2%
e 5788992
 
9.6%
t 4882790
 
8.1%
a 4472087
 
7.4%
, 4177337
 
7.0%
4177337
 
7.0%
r 4173639
 
6.9%
o 3451884
 
5.7%
h 2166498
 
3.6%
n 2110119
 
3.5%
Other values (41) 17970004
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60089604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6718917
 
11.2%
e 5788992
 
9.6%
t 4882790
 
8.1%
a 4472087
 
7.4%
, 4177337
 
7.0%
4177337
 
7.0%
r 4173639
 
6.9%
o 3451884
 
5.7%
h 2166498
 
3.6%
n 2110119
 
3.5%
Other values (41) 17970004
29.9%
Distinct3
Distinct (%)< 0.1%
Missing257
Missing (%)0.1%
Memory size3.5 MiB
2025-03-26T16:29:13.627414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.999978112
Min length7

Characters and Unicode

Total characters3654998
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 456848
> 99.9%
animalis 18
 
< 0.1%
animala 10
 
< 0.1%
2025-03-26T16:29:13.707423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 913742
25.0%
a 913734
25.0%
A 456876
12.5%
n 456876
12.5%
m 456876
12.5%
l 456876
12.5%
s 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3654998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 913742
25.0%
a 913734
25.0%
A 456876
12.5%
n 456876
12.5%
m 456876
12.5%
l 456876
12.5%
s 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3654998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 913742
25.0%
a 913734
25.0%
A 456876
12.5%
n 456876
12.5%
m 456876
12.5%
l 456876
12.5%
s 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3654998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 913742
25.0%
a 913734
25.0%
A 456876
12.5%
n 456876
12.5%
m 456876
12.5%
l 456876
12.5%
s 18
 
< 0.1%

phylum
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing241
Missing (%)0.1%
Memory size3.5 MiB
2025-03-26T16:29:13.734852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3655136
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChordata
2nd rowChordata
3rd rowChordata
4th rowChordata
5th rowChordata
ValueCountFrequency (%)
chordata 456892
100.0%
2025-03-26T16:29:13.811618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 913784
25.0%
C 456892
12.5%
h 456892
12.5%
o 456892
12.5%
r 456892
12.5%
d 456892
12.5%
t 456892
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 913784
25.0%
C 456892
12.5%
h 456892
12.5%
o 456892
12.5%
r 456892
12.5%
d 456892
12.5%
t 456892
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 913784
25.0%
C 456892
12.5%
h 456892
12.5%
o 456892
12.5%
r 456892
12.5%
d 456892
12.5%
t 456892
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 913784
25.0%
C 456892
12.5%
h 456892
12.5%
o 456892
12.5%
r 456892
12.5%
d 456892
12.5%
t 456892
12.5%

class
Text

Distinct7
Distinct (%)< 0.1%
Missing287
Missing (%)0.1%
Memory size3.5 MiB
2025-03-26T16:29:13.839313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.00337094
Min length6

Characters and Unicode

Total characters6397384
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActinopterygii
2nd rowActinopterygii
3rd rowActinopterygii
4th rowActinopterygii
5th rowActinopterygii
ValueCountFrequency (%)
actinopterygii 446336
97.7%
chondrichthyes 9226
 
2.0%
cephalaspidomorphi 568
 
0.1%
cephalochordata 518
 
0.1%
myxini 151
 
< 0.1%
sarcopterygii 42
 
< 0.1%
elasmobranchii 5
 
< 0.1%
2025-03-26T16:29:14.011780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1349766
21.1%
t 902458
14.1%
o 457781
 
7.2%
r 456737
 
7.1%
e 456690
 
7.1%
c 456127
 
7.1%
y 455755
 
7.1%
n 455718
 
7.1%
p 448600
 
7.0%
g 446378
 
7.0%
Other values (13) 511374
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6397384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1349766
21.1%
t 902458
14.1%
o 457781
 
7.2%
r 456737
 
7.1%
e 456690
 
7.1%
c 456127
 
7.1%
y 455755
 
7.1%
n 455718
 
7.1%
p 448600
 
7.0%
g 446378
 
7.0%
Other values (13) 511374
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6397384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1349766
21.1%
t 902458
14.1%
o 457781
 
7.2%
r 456737
 
7.1%
e 456690
 
7.1%
c 456127
 
7.1%
y 455755
 
7.1%
n 455718
 
7.1%
p 448600
 
7.0%
g 446378
 
7.0%
Other values (13) 511374
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6397384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1349766
21.1%
t 902458
14.1%
o 457781
 
7.2%
r 456737
 
7.1%
e 456690
 
7.1%
c 456127
 
7.1%
y 455755
 
7.1%
n 455718
 
7.1%
p 448600
 
7.0%
g 446378
 
7.0%
Other values (13) 511374
 
8.0%

order
Text

Distinct71
Distinct (%)< 0.1%
Missing442
Missing (%)0.1%
Memory size3.5 MiB
2025-03-26T16:29:14.052408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length19
Mean length12.45527501
Min length9

Characters and Unicode

Total characters5688212
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAnguilliformes
2nd rowPerciformes
3rd rowPerciformes
4th rowCypriniformes
5th rowPerciformes
ValueCountFrequency (%)
perciformes 214688
47.0%
cypriniformes 33875
 
7.4%
scorpaeniformes 17718
 
3.9%
characiformes 17539
 
3.8%
anguilliformes 17213
 
3.8%
siluriformes 14342
 
3.1%
myctophiformes 13781
 
3.0%
pleuronectiformes 12379
 
2.7%
stomiiformes 12133
 
2.7%
tetraodontiformes 10571
 
2.3%
Other values (61) 92452
20.2%
2025-03-26T16:29:14.148605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 815170
14.3%
e 767831
13.5%
o 591431
10.4%
i 569259
10.0%
m 479162
8.4%
s 466189
8.2%
f 456691
8.0%
c 294831
 
5.2%
P 228415
 
4.0%
n 147029
 
2.6%
Other values (27) 872204
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5688212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 815170
14.3%
e 767831
13.5%
o 591431
10.4%
i 569259
10.0%
m 479162
8.4%
s 466189
8.2%
f 456691
8.0%
c 294831
 
5.2%
P 228415
 
4.0%
n 147029
 
2.6%
Other values (27) 872204
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5688212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 815170
14.3%
e 767831
13.5%
o 591431
10.4%
i 569259
10.0%
m 479162
8.4%
s 466189
8.2%
f 456691
8.0%
c 294831
 
5.2%
P 228415
 
4.0%
n 147029
 
2.6%
Other values (27) 872204
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5688212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 815170
14.3%
e 767831
13.5%
o 591431
10.4%
i 569259
10.0%
m 479162
8.4%
s 466189
8.2%
f 456691
8.0%
c 294831
 
5.2%
P 228415
 
4.0%
n 147029
 
2.6%
Other values (27) 872204
15.3%

family
Text

Distinct556
Distinct (%)0.1%
Missing898
Missing (%)0.2%
Memory size3.5 MiB
2025-03-26T16:29:14.246632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length17
Mean length10.77616141
Min length6

Characters and Unicode

Total characters4916462
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st rowMuraenidae
2nd rowMugilidae
3rd rowGobiidae
4th rowCyprinidae
5th rowCentropomidae
ValueCountFrequency (%)
cyprinidae 27741
 
6.1%
gobiidae 26121
 
5.7%
pomacentridae 16272
 
3.6%
labridae 14701
 
3.2%
blenniidae 14557
 
3.2%
myctophidae 13625
 
3.0%
apogonidae 12430
 
2.7%
serranidae 11444
 
2.5%
characidae 10673
 
2.3%
stomiidae 7695
 
1.7%
Other values (546) 300976
66.0%
2025-03-26T16:29:14.415119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 712985
14.5%
e 652559
13.3%
i 648521
13.2%
d 490851
10.0%
o 278518
 
5.7%
r 276743
 
5.6%
n 253426
 
5.2%
t 212237
 
4.3%
c 160606
 
3.3%
h 140982
 
2.9%
Other values (39) 1089034
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4916462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 712985
14.5%
e 652559
13.3%
i 648521
13.2%
d 490851
10.0%
o 278518
 
5.7%
r 276743
 
5.6%
n 253426
 
5.2%
t 212237
 
4.3%
c 160606
 
3.3%
h 140982
 
2.9%
Other values (39) 1089034
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4916462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 712985
14.5%
e 652559
13.3%
i 648521
13.2%
d 490851
10.0%
o 278518
 
5.7%
r 276743
 
5.6%
n 253426
 
5.2%
t 212237
 
4.3%
c 160606
 
3.3%
h 140982
 
2.9%
Other values (39) 1089034
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4916462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 712985
14.5%
e 652559
13.3%
i 648521
13.2%
d 490851
10.0%
o 278518
 
5.7%
r 276743
 
5.6%
n 253426
 
5.2%
t 212237
 
4.3%
c 160606
 
3.3%
h 140982
 
2.9%
Other values (39) 1089034
22.2%

genus
Text

Missing 

Distinct5473
Distinct (%)1.3%
Missing23392
Missing (%)5.1%
Memory size3.5 MiB
2025-03-26T16:29:14.533020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length19
Mean length9.850855695
Min length2

Characters and Unicode

Total characters4272720
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique862 ?
Unique (%)0.2%

Sample

1st rowEchidna
2nd rowMugil
3rd rowCryptocentrus
4th rowRhinichthys
5th rowCentropomus
ValueCountFrequency (%)
notropis 7221
 
1.7%
etheostoma 4871
 
1.1%
gymnothorax 4347
 
1.0%
lepomis 4294
 
1.0%
chaetodon 4265
 
1.0%
lutjanus 3830
 
0.9%
halichoeres 3141
 
0.7%
chromis 3135
 
0.7%
pomacentrus 2968
 
0.7%
acanthurus 2907
 
0.7%
Other values (5466) 392891
90.6%
2025-03-26T16:29:14.717912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 403099
 
9.4%
s 400562
 
9.4%
a 335137
 
7.8%
i 300988
 
7.0%
e 277367
 
6.5%
r 260738
 
6.1%
t 247548
 
5.8%
u 245854
 
5.8%
n 221415
 
5.2%
h 208572
 
4.9%
Other values (45) 1371440
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4272720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 403099
 
9.4%
s 400562
 
9.4%
a 335137
 
7.8%
i 300988
 
7.0%
e 277367
 
6.5%
r 260738
 
6.1%
t 247548
 
5.8%
u 245854
 
5.8%
n 221415
 
5.2%
h 208572
 
4.9%
Other values (45) 1371440
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4272720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 403099
 
9.4%
s 400562
 
9.4%
a 335137
 
7.8%
i 300988
 
7.0%
e 277367
 
6.5%
r 260738
 
6.1%
t 247548
 
5.8%
u 245854
 
5.8%
n 221415
 
5.2%
h 208572
 
4.9%
Other values (45) 1371440
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4272720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 403099
 
9.4%
s 400562
 
9.4%
a 335137
 
7.8%
i 300988
 
7.0%
e 277367
 
6.5%
r 260738
 
6.1%
t 247548
 
5.8%
u 245854
 
5.8%
n 221415
 
5.2%
h 208572
 
4.9%
Other values (45) 1371440
32.1%

subgenus
Text

Missing 

Distinct57
Distinct (%)19.7%
Missing456844
Missing (%)99.9%
Memory size3.5 MiB
2025-03-26T16:29:14.777398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.709342561
Min length2

Characters and Unicode

Total characters2806
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)6.2%

Sample

1st rowEcsenius
2nd rowWatasea
3rd rowDistoechodon
4th rowEcsenius
5th rowArtediellops
ValueCountFrequency (%)
meiacanthus 49
17.0%
ecsenius 34
 
11.8%
dasson 31
 
10.7%
watasea 12
 
4.2%
allomeiacanthus 10
 
3.5%
hadropterus 9
 
3.1%
odontohypopomus 8
 
2.8%
musgravius 8
 
2.8%
ulocentra 7
 
2.4%
hyporhamphus 6
 
2.1%
Other values (47) 115
39.8%
2025-03-26T16:29:14.881249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 331
11.8%
a 313
11.2%
o 228
 
8.1%
n 200
 
7.1%
e 194
 
6.9%
u 191
 
6.8%
i 187
 
6.7%
t 171
 
6.1%
c 137
 
4.9%
h 121
 
4.3%
Other values (33) 733
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 331
11.8%
a 313
11.2%
o 228
 
8.1%
n 200
 
7.1%
e 194
 
6.9%
u 191
 
6.8%
i 187
 
6.7%
t 171
 
6.1%
c 137
 
4.9%
h 121
 
4.3%
Other values (33) 733
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 331
11.8%
a 313
11.2%
o 228
 
8.1%
n 200
 
7.1%
e 194
 
6.9%
u 191
 
6.8%
i 187
 
6.7%
t 171
 
6.1%
c 137
 
4.9%
h 121
 
4.3%
Other values (33) 733
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 331
11.8%
a 313
11.2%
o 228
 
8.1%
n 200
 
7.1%
e 194
 
6.9%
u 191
 
6.8%
i 187
 
6.7%
t 171
 
6.1%
c 137
 
4.9%
h 121
 
4.3%
Other values (33) 733
26.1%

specificEpithet
Text

Missing 

Distinct13217
Distinct (%)3.4%
Missing67592
Missing (%)14.8%
Memory size3.5 MiB
2025-03-26T16:29:14.995448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length24
Mean length8.887570243
Min length2

Characters and Unicode

Total characters3462073
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3138 ?
Unique (%)0.8%

Sample

1st rownebulosa
2nd rowfilifer
3rd rowcataractae
4th rowensiferus
5th rowinferomaculata
ValueCountFrequency (%)
maculatus 1824
 
0.5%
fasciatus 1660
 
0.4%
lineatus 1596
 
0.4%
punctatus 1565
 
0.4%
affinis 1533
 
0.4%
nigricans 1454
 
0.4%
ocellatus 1441
 
0.4%
cornutus 1265
 
0.3%
notatus 1169
 
0.3%
niger 1167
 
0.3%
Other values (13191) 375036
96.2%
2025-03-26T16:29:15.186823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 389539
11.3%
s 362037
10.5%
i 360778
10.4%
u 279408
 
8.1%
e 244213
 
7.1%
r 230175
 
6.6%
t 217446
 
6.3%
n 209515
 
6.1%
o 196527
 
5.7%
l 194928
 
5.6%
Other values (33) 777507
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3462073
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 389539
11.3%
s 362037
10.5%
i 360778
10.4%
u 279408
 
8.1%
e 244213
 
7.1%
r 230175
 
6.6%
t 217446
 
6.3%
n 209515
 
6.1%
o 196527
 
5.7%
l 194928
 
5.6%
Other values (33) 777507
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3462073
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 389539
11.3%
s 362037
10.5%
i 360778
10.4%
u 279408
 
8.1%
e 244213
 
7.1%
r 230175
 
6.6%
t 217446
 
6.3%
n 209515
 
6.1%
o 196527
 
5.7%
l 194928
 
5.6%
Other values (33) 777507
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3462073
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 389539
11.3%
s 362037
10.5%
i 360778
10.4%
u 279408
 
8.1%
e 244213
 
7.1%
r 230175
 
6.6%
t 217446
 
6.3%
n 209515
 
6.1%
o 196527
 
5.7%
l 194928
 
5.6%
Other values (33) 777507
22.5%

infraspecificEpithet
Text

Missing 

Distinct914
Distinct (%)9.2%
Missing447182
Missing (%)97.8%
Memory size3.5 MiB
2025-03-26T16:29:15.314094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length16
Mean length8.893880012
Min length2

Characters and Unicode

Total characters88503
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)3.1%

Sample

1st rowniloticus
2nd rowramosus
3rd rowvexillare
4th rowvermiculatus
5th rowsciera
ValueCountFrequency (%)
leptocephalus 303
 
3.0%
atromaculatus 225
 
2.3%
crocodilus 222
 
2.2%
atratulus 170
 
1.7%
vermiculatus 156
 
1.6%
ferox 146
 
1.5%
commersoni 138
 
1.4%
purpurescens 135
 
1.4%
salmoides 125
 
1.3%
interocularis 121
 
1.2%
Other values (905) 8213
82.5%
2025-03-26T16:29:15.495020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 9818
11.1%
a 9363
10.6%
i 8025
9.1%
u 7990
9.0%
e 6203
 
7.0%
r 6090
 
6.9%
o 5932
 
6.7%
l 5760
 
6.5%
c 5265
 
5.9%
t 5122
 
5.8%
Other values (21) 18935
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 9818
11.1%
a 9363
10.6%
i 8025
9.1%
u 7990
9.0%
e 6203
 
7.0%
r 6090
 
6.9%
o 5932
 
6.7%
l 5760
 
6.5%
c 5265
 
5.9%
t 5122
 
5.8%
Other values (21) 18935
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 9818
11.1%
a 9363
10.6%
i 8025
9.1%
u 7990
9.0%
e 6203
 
7.0%
r 6090
 
6.9%
o 5932
 
6.7%
l 5760
 
6.5%
c 5265
 
5.9%
t 5122
 
5.8%
Other values (21) 18935
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 9818
11.1%
a 9363
10.6%
i 8025
9.1%
u 7990
9.0%
e 6203
 
7.0%
r 6090
 
6.9%
o 5932
 
6.7%
l 5760
 
6.5%
c 5265
 
5.9%
t 5122
 
5.8%
Other values (21) 18935
21.4%

taxonRank
Text

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing447182
Missing (%)97.8%
Memory size3.5 MiB
2025-03-26T16:29:15.539932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters99510
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsubspecies
2nd rowsubspecies
3rd rowsubspecies
4th rowsubspecies
5th rowsubspecies
ValueCountFrequency (%)
subspecies 9951
100.0%
2025-03-26T16:29:15.617761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 29853
30.0%
e 19902
20.0%
u 9951
 
10.0%
b 9951
 
10.0%
p 9951
 
10.0%
c 9951
 
10.0%
i 9951
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 29853
30.0%
e 19902
20.0%
u 9951
 
10.0%
b 9951
 
10.0%
p 9951
 
10.0%
c 9951
 
10.0%
i 9951
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 29853
30.0%
e 19902
20.0%
u 9951
 
10.0%
b 9951
 
10.0%
p 9951
 
10.0%
c 9951
 
10.0%
i 9951
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 29853
30.0%
e 19902
20.0%
u 9951
 
10.0%
b 9951
 
10.0%
p 9951
 
10.0%
c 9951
 
10.0%
i 9951
 
10.0%
Distinct1553
Distinct (%)4.0%
Missing417908
Missing (%)91.4%
Memory size3.5 MiB
2025-03-26T16:29:15.740183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length29
Mean length11.04441045
Min length2

Characters and Unicode

Total characters433217
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique384 ?
Unique (%)1.0%

Sample

1st rowVari et al.
2nd rowLarson
3rd rowEvermann & Goldsborough
4th rowRandall & Swerdloff
5th rowGirard
ValueCountFrequency (%)
16182
 
21.1%
randall 3025
 
3.9%
jordan 2710
 
3.5%
gilbert 2290
 
3.0%
et 2251
 
2.9%
al 2251
 
2.9%
schultz 2146
 
2.8%
bean 1682
 
2.2%
springer 1561
 
2.0%
gill 1204
 
1.6%
Other values (980) 41365
54.0%
2025-03-26T16:29:15.940937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 39829
 
9.2%
37442
 
8.6%
a 36983
 
8.5%
l 31237
 
7.2%
r 30624
 
7.1%
n 30119
 
7.0%
i 21886
 
5.1%
o 19267
 
4.4%
t 17505
 
4.0%
& 16182
 
3.7%
Other values (50) 152143
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 433217
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 39829
 
9.2%
37442
 
8.6%
a 36983
 
8.5%
l 31237
 
7.2%
r 30624
 
7.1%
n 30119
 
7.0%
i 21886
 
5.1%
o 19267
 
4.4%
t 17505
 
4.0%
& 16182
 
3.7%
Other values (50) 152143
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 433217
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 39829
 
9.2%
37442
 
8.6%
a 36983
 
8.5%
l 31237
 
7.2%
r 30624
 
7.1%
n 30119
 
7.0%
i 21886
 
5.1%
o 19267
 
4.4%
t 17505
 
4.0%
& 16182
 
3.7%
Other values (50) 152143
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 433217
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 39829
 
9.2%
37442
 
8.6%
a 36983
 
8.5%
l 31237
 
7.2%
r 30624
 
7.1%
n 30119
 
7.0%
i 21886
 
5.1%
o 19267
 
4.4%
t 17505
 
4.0%
& 16182
 
3.7%
Other values (50) 152143
35.1%